Search traffic is no longer a simple Google SERP game. In March 2025, Google AI Overviews appeared on 13.14 % of U.S. results pages, while ChatGPT, CoPilot, Claude, Perplexity, and friends attracted more than 600 million unique visitors in a single month.

The trouble for search visibility within the era of AI is this—when LLMs summarize a topic, they pick only a handful of brand mentions or product recommendations. If your company isn’t in the short‑listed answer, you are invisible.

How are brands to keep up?

Large Language Model Optimization (LLMO) has become essential for maintaining brand visibility. 60% of consumers are now using AI chatbots for product research, and traditional web clicks have decreased by 34% due to user reliance on AI-generated responses.

Traditional SEO suites weren’t designed to measure—or shape—how models present your brand. LLMO tools give marketers a purpose‑built radar and action layer for the AI answer economy.

This comprehensive analysis examines the top 9 LLMO software platforms that marketers, SEO professionals, and content specialists need to understand in today’s AI-driven search environment.

First, Let’s Define the Category: What the Hell Is LLMO?

LLMO is an emerging discipline of marketing that focuses on optimizing for brand visibility and performance on AI surfaces and large language models like ChatGPT, Gemini, and Claude.

Every product profiled below checked at least three of the five boxes as of the evaluation date, June  2025.

1. Goodie AI

The AEO Pioneer That Set the LLMO Bar

Goodie launched as an Answer Engine Optimization platform and has evolved into a full LLMO control tower. Its core modules include AI Visibility Monitoring, an Optimization Hub that prescribes prompt‑level fixes, a Topic Explorer fed by multi‑LLM data, and a Traffic & Attribution suite linking citations to conversions.

The dashboard covers ChatGPT, Gemini, Claude, Perplexity, DeepSeek, and Google AI Overviews out of the box, and its 15‑factor “AEO Periodic Table” gives teams a science‑backed checklist for boosting mention share.

The highlights:

• It is the most comprehensive platform in the space, covering the most models and features needed to effectively execute an LLMO strategy.
• Deep competitive benchmarking for brands (think SimilarWeb for AI answers).
• Action layer includes multistep outreach agents that can pitch journalists, update Wikipedia, or push structured brand‑verified data feeds to model providers.
• Clean UI built for SEO, PR and growth teams—not just data scientists.

For pro marketers, Goodie AI provides the most comprehensive dashboards for AI search performance metrics, competitor benchmarking, and content gap analysis. The platform’s integrated approach combines monitoring with actionable optimization recommendations, making it ideal for CMOs and brand managers who need real-time insights into AI search performance while maintaining control over brand narrative across AI platforms.

Best for: Mid‑market to enterprise brands that need one pane of glass for monitoring + action.

Pricing: Subscription-based with lower tiers starting at $950. Custom pricing available upon request.

2. Ahrefs

An SEO Powerhouse Now Tuned for LLMO

Ahrefs rolled out some early LLMO features into existing tools like Site Explorer and Content Explorer. You can filter keywords that trigger AI Overviews, see which of your pages are cited in ChatGPT or Perplexity, and run backlink campaigns targeted at the sources that LLMs quote most.

The highlights:

• Giant 10B keyword dataset now flags SERP features labelled AI.
• “LLMO Opportunities” report surfaces unlinked brand mentions inside popular Q&A sites, Reddit threads, and news stories to seed future model training.

Limitations: No prompt‑level sentiment scoring and no optimization workflow—you’ll still need manual outreach. Ahrefs is still primarily focused on conventional SEO.

Best for: Organizations who are already leveraging Ahrefs in their marketing toolkit and are looking to incorporate elements of LLMO.

Pricing: Seamless for existing Ahrefs users—no extra contract. Pricing for Ahrefs starts at $108 per month for an annual contract.

3. Evertune AI

Research‑Grade Brand Perception Analytics

Evertune AI treats LLMs as survey respondents. Its engine autogenerates thousands of category‑specific prompts, captures the answers, then distills the data into insights about share of voice, sentiment polarity, and reasoning chains.

The highlights:

• Prompt panels allow users to choose personas (ex. “CFO evaluating ERP software”) to see how positioning shifts.
• Bias flagging that alerts when models use outdated pricing or misinformation.
• Export‑ready charts for board decks.

Best for: Fortune 1000 communications teams and investor‑relations groups that need brand‑perception telemetry.

Pricing: Pricing for Evertune AI is custom and not readily available on their website.

4. Semrush

An AI Toolkit Added to a Familiar Swiss Army Knife

Semrush’s new AI Visibility widgets live inside Position Tracking and the Enterprise AIO module. You can monitor how often AI Overviews cite your domain, discover the common citation sources in your niche (Quora and Reddit dominate today) and calculate the 4.4× higher value of AI visitors vs. traditional organic sessions.

The highlights:

• Combines traditional SEO with emerging LLMO capabilities.
• Integration with ContentShake and backlink outreach means fixes sit one click away.
• Provides extensive educational resources and certification programs, with documented case studies across multiple industries.

Note: Visibility is Google‑centric; coverage of ChatGPT or Claude is thinner than pure‑play LLMO suites.

Limitations: Semrush entered the game a bit late, so the product is still largely traditional SEO focused and may fall short in terms of feature parity compared to other players. Additionally, the comprehensive feature set creates complexity that may overwhelm smaller businesses, and many advanced features require additional subscriptions beyond base plans

Best for: If your org already relies on Semrush, enabling the AI tab is friction‑free. Also ideal for teams needing comprehensive marketing toolkits.

Pricing: Semrush pricing is largely custom and varies depending on the specific toolkits your team needs.

5. LLMO Metrics

Laser‑Focused on Generative‑Engine KPIs

LLMO Metrics positions its solution as helping companies “be the brand AI recommends.” It tracks answer accuracy, brand‑mention frequency, and source bias across ChatGPT, Gemini, CoPilot, and more. Its “Prompt Search Volume” panel gives a sense of demand—how many people are asking a query inside chat products.

The highlights:

• One‑click “Fix” cards that link a problematic answer to suggested PR targets or on‑page schema tweaks.
• Role‑based dashboards for PR vs. SEO vs. product.
• Free tier to monitor 25 prompts—useful for startups.

Best for: Multi-discipline teams, particularly for SaaS and technology products.

Pricing: LLMO Metrics offers a freemium model, with paid plans starting at 72 Euros per month when billed annually.

6. Peec AI

SMB‑Friendly Analytics With Slick UX

Founded in 2025 in Berlin with $2.01M pre-seed funding from Angel Invest, Antler, Combination VC, and other tier-1 investors, Peec AI represents cutting-edge innovation in the LLMO space.

Despite being extremely new with only three employees at the time of writing this article, Peec AI has gained recognition as one of the early GEO/LLMO tools to differentiate between brand visibility and source citations.

Peec brings a polished, almost Mixpanel‑like interface to AI answer tracking. Line/bar views show week‑over‑week visibility shifts, and underperforming prompts are flagged in red.

The highlights:

• Industry‑ranking leader‑boards (great for investor updates).
• Slack alerts when your share of voice dips below threshold.
• White‑label PDF exports for agencies.

Best for: Agencies looking to integrate LLMO into their service offering and small, specialized teams in need of real-time updates.

Pricing: With plan prices between $79 and $299 per month, it’s the most affordable option on our list.

7. Bluefish AI

Brand‑Safety Meets AI Marketing Agents

Backed by Crane and Bloomberg Beta, Bluefish AI offers an AI Marketing Suite that marries brand‑safety monitoring with data‑syndication pipelines. It even allows brands to push brand‑verified data feeds directly into model providers’ ingestion queues. A separate chatbot platform helps enterprises run on‑site assistants that echo the approved messaging.

The highlights:

• “AI Funnel Consumer Insights” shows how different audience segments see you across LLMs.
• Custom AI agents can run agent campaigns—think interactive advertorials inside chat interfaces.

The platform’s comprehensive approach combines monitoring, optimization, and engagement capabilities. Industry expertise from team members with proven MarTech experience and backing from top-tier AI-focused investors provide strong credibility. However, limited public case studies and pricing opacity may challenge evaluation processes.

Best for: Enterprises, likely with specialized AI and search teams, who are looking to rapidly scale up their LLMO efforts.

Pricing: High‑touch, starting at around $4,000 per month after demo.

8. daydream

Full‑Spectrum GEO/LLMO Services + In-House Tech

daydream operates as a full-service SEO and GEO agency rather than a traditional software platform. The company was founded in 2023 in San Francisco with $3.8M seed funding from First Round Capital, Basis Set Ventures, WndrCo, and Hustle Fund. Led by recognized SEO experts, the agency specializes in programmatic SEO and AI-driven content optimization.

Daydream began as a programmatic SEO agency; v2.0 adds Generative Engine Optimization (GEO) services, open‑source frameworks for LLMs.txt, and a proprietary visibility‑score model.

The highlights:

• Thought‑leadership depth—its public library dissects how Gemini, Claude, and Perplexity crawl and cite content.
• It combines editorial SEO, technical SEO, and LLM‑specific data structuring under one retainer.

Best for: Teams who would rather outsource their LLMO and GEO efforts than license software for an internal team.

Pricing: Pricing is custom after a 30-minute discovery call with their team.

9. Soar.sh

Community‑Driven LLM SEO for Reddit & Quora

Soar is technically an agency, but its LLM SEO capabilities earn it a place because it solves a unique slice of optimization: getting your brand into the training data by dominating Reddit and Quora threads.

The highlights:

• They create and manage Reddit threads that often rank in organic Google and feed LLMs.
• They also offer Subreddit acquisition and Discord community programs that seed long‑tail, high‑trust UGC—which content language models love.

Best for: For companies who are already leveraging one of the analytics-heavy platforms listed above, Soar.sh works well as a complement.

Pricing: Pricing is custom through a proposal request Typeform on their website.

Side‑by‑Side Snapshot (Scores Out of 5)

How to Pick the Right Large Language Model Optimization (LLMO) Software

1. If you need an end‑to‑end control center: Goodie AI or Bluefish AI dominate, with Goodie AI skewing analytics‑first and Bluefish AI skewing campaign‑heavy.
2. If you already pay for a leading SEO suite: Enable the LLMO modules inside Ahrefs or Semrush before adding another subscription.
3. If your budget is tight, but you need instant visibility charts: Peec’s $79 starter is unbeatable.
4. If you care about why models like or dislike you: Evertune’s sentiment deep‑dives are gold.
5. If your goal is simply to flood the data layer: Pair Soar’s Reddit operations with any tracker.

Final Thoughts on the Growing LLMO Software Space

LLMO software is evolving at an incredible speed, especially now with VC money pouring in, but the fundamentals stay constant: monitor how models mention you, understand why they do it, and feed them cleaner, higher‑authority signals than your competitors.

Pick a platform (or combination of tools and services) that closes all three loops and start optimizing now—while your competitors are still arguing about keyword density.

The post The Marketer’s Guide to the Best LLMO Software [2025] appeared first on NoGood™: Growth Marketing Agency.

This guide breaks down what GEO actually means, how to measure performance, and which GEO tools are leading the pack. Whether you’re a marketer, founder, or SEO pro, we did the heavy lifting so you don’t have to – here’s everything you need to navigate this next chapter of search.

What is Generative Search Optimization (GEO)?

GEO involves optimizing your content to appear prominently in responses generated by AI search engines. Unlike traditional SEO, which targets rankings in search engine results pages, GEO ensures your content is cited or mentioned within AI-generated responses.

GEO focuses on:

• Improving clarity, consistency, and accuracy of content.
• Ensuring your brand is consistently referenced by AI models.
• Crafting content optimized for AI’s contextual understanding.
• Building a website optimized for AI crawlers and agents.

Goodie AI recently published a study, the AEO periodic table on AI search visibility ranking factors, that offers insights into the key variables that impact GEO.

How to Measure GEO Performance

Here are key GEO metrics to monitor:

• AI Visibility Score: Frequency of your brand or content appearing in AI-generated responses.
• Source Citations: How often AI platforms reference your content.
• Competitive Benchmarking: Comparing your visibility against competitors.
• Sentiment Analysis: Ensuring AI accurately and positively portrays your brand.

Tracking these metrics provides insights into your GEO strategy’s effectiveness and helps identify areas for improvement.

Overview of the Market for GEO Tools

Features

Many of the tools we reviewed offer visibility monitoring features, but some are more detailed and extensive than others. The main methodology behind measuring this centers on analyzing multiple prompts and providing insight about a brand’s visibility or lack thereof in the responses to those prompts.

Optimization Capabilities

Most of the current players lack strong optimization capabilities. While they provide helpful insights, those insights are not always coupled with actionable optimization opportunities. Some, however, are more advanced in that regard than others.

Pricing

The space is split between enterprise tools and small business light tools. The light tools are not as accurate and tend to only focus on the visibility aspects of GEO. We found that the enterprise tools like Goodie, Scrunch, and Profound have more capabilities and offer more value, but are also more expensive.

GEO Market Dynamics

There are many new emerging players, so we’ll continue to update this list as the market changes.

New York City houses many of the current players and is rapidly becoming the GEO capital of the world.

How to Choose the Right GEO Tool

When selecting a GEO tool, consider:

• Platform Coverage: Support for major AI platforms (ChatGPT, Perplexity, Google Gemini).
• Real-Time Monitoring: Quick updates and alerts on brand mentions.
• Actionable Insights: Clear guidance on content optimization.
• Integration: Compatibility with existing SEO and analytics tools.
• Ease of Use: Intuitive dashboards and straightforward data interpretation.
• Pricing and Scalability: Transparent pricing and flexibility to scale with your needs.

Top 9 Generative Search Optimization Tools in 2025

1. Goodie AI

Description: Goodie AI is a pioneering AI native platform specializing in Generative Engine Optimization. It helps brands enhance their visibility across AI answer engines like ChatGPT, Gemini, and Perplexity.

Pricing: They offer multiple tiers for startups and enterprises; the exact pricing structure is available upon request.

Founded: 2023

Headquarters: New York City, USA

GEO Features

• AI Visibility Monitoring: Tracks brand visibility and mentions across various LLMs and AI search platforms.
• AI Optimization Hub: Identifies opportunities and provides actionable recommendations to improve AI search visibility.
• AI Content Writer: Identifies gaps and assists in creating content optimized for AI search engines.
• AI Search Analytics & Attribution: Measures and attributes your AI search performance back to the metrics that matter for your business, whether that be user growth or revenue growth.

Pros

• Comprehensive suite of tools tailored for AI search optimization.
• User-friendly interface with real-time analytics.
• Measurement for most major AI platforms, including DeepSeek and Claude.
• Access to an Optimization Hub that’s highly precise and provides actionable GEO insights for each tracked source.

Cons

• As a newer platform, it may lack some integrations with existing marketing tools.
• The tool doesn’t have a report export feature (yet).

2. Semrush

SEMrush recently launched its GEO tool, and it’s currently only available to enterprises. They came late to this GEO tool race and primarily focus on the visibility aspect of things. That being said, they’re the leading traditional SEO tool with massive data capabilities and a large customer base.

• Key Features: Visibility analysis, robust keyword research, competitive analysis, and AI result monitoring on Google.
• Pros: Extensive suite, reliable, user-friendly.
• Cons: Limited to Google-centric AI features, indirect GEO insights.
• Pricing: Starts at $120/month.
• Founded: 2008
• Headquarters: Boston, USA

3. Scrunch AI

Scrunch AI delivers a comprehensive enterprise-focused approach to monitoring and optimizing brand visibility within AI search ecosystems. In a world where users want answers instead of links, Scrunch AI offers a platform to help businesses give users what they’re looking for while boosting your brand’s visibility.

• Key Features: Real-time visibility monitoring, search journey mapping of AI-driven customer interactions, proactive hallucination and misinformation detection.
• Pros: Extensive insights, ideal for large enterprises.
• Cons: Not suitable for small businesses, no self-service option available.
• Pricing: Custom pricing offered upon request.
• Founded: 2022
• Headquarters: Salt Lake City, USA

4. Peec AI

Peec AI simplifies AI analytics with a straightforward approach, ideal for small-to-medium businesses and marketing agencies. Peec AI’s versatility makes it applicable across multiple industries such as healthcare, finance, and marketing. Peec’s dashboard is also user-friendly, making it a great choice if you aren’t an expert in AI platforms.

• Key Features: AI visibility metrics, source citation tracking, competitive benchmarking.
• Pros: Easy to use, clear analytics, affordable.
• Cons: Basic feature set, lacks advanced options like in-depth sentiment tracking or optimization recommendations.
• Pricing: From €120/month.
• Founded: 2023
• Headquarters: Berlin, Germany

5. Otterly AI

Otterly AI is an intuitive and accessible AI search tool designed for monitoring and optimizing your generative engine presence. It’s particularly suitable for smaller businesses and startups that are primarily focused on the monitoring side of the equation.

• Key Features: AI visibility tracking, sentiment analysis, and monitoring link citations.
• Pros: User-friendly interface, affordable, good for beginners.
• Cons: Limited recommendation capabilities, basic analytics.
• Pricing: Starts at $49/month.
• Founded: 2023
• Headquarters: Vienna, Austria

6. Profound

Profound provides a wide range of in-depth GEO analytics specifically tailored to large, global enterprises. One of the early movers in the space, they raised a seed round for Khosla Ventures and South Park Commons, among other notable VCs.

• Key Features: AI visibility tracking, detailed conversation and sentiment analysis, proactive crawler optimization.
• Pros: Highly detailed and robust analytics, multilingual support.
• Cons: Only suitable for enterprises, lacks SEO and content features, and is premium-priced.
• Pricing: Custom pricing upon request. For more information about pricing structure, reach out to Profound’s sales team.
• Founded: 2024
• Headquarters: New York City, USA

7. Hubspot’s AI Search Grader

HubSpot’s AI Search Grader is designed to evaluate content’s search engine performance, providing insights and generative suggestions to help content rank better. It combines AI-driven analysis with SEO expertise, giving users a powerful tool to improve their content strategy.

Instead of just focusing on traditional SEO metrics, it also evaluates the semantic relevance of your content. This includes how well your content aligns with what AI-powered search engines understand in terms of context and user intent.

• Key Features: Provides suggestions to optimize content for better rankings within AI search, compares your content to competitors, and evaluates for conversational AI.
• Pros: Excellent for improving content visibility within AI search.
• Cons: Doesn’t account for all of the AI search algorithms, lacks deeper customization abilities.
• Pricing: Part of HubSpot’s Marketing Hub. Starts at $23 a month and ranges up to $3,200 depending on your subscription tier.
• Founded: This tool was launched in 2023.
• Headquarters: Cambridge, MA.

8. Athena HQ

Athena HQ is an AI search visibility platform designed to assess and enhance the search engine performance of your content. Leveraging advanced AI algorithms, Athena HQ offers in-depth analysis of your website’s visibility, providing actionable insights and tailored suggestions to help your content rank higher in search results. Athena HQ’s tool also evaluates semantic relevance, which determines how well-aligned your content is within the given context.

• Key Features: AI-driven content analysis, semantic relevance evaluation, SEO recommendations, insights into user intent, content performance tracking.
• Pros: Can easily be integrated with other SEO tools and an easy-to-understand reporting dashboard.
• Cons: Limited customization capabilities for niche industries, doesn’t offer a complete GEO/SEO solution.
• Pricing: $400 for the lite version and $900 for their growth version. Also offers an enterprise version for custom pricing options.
• Founded: 2025
• Headquarters: San Francisco, CA

9. Ahrefs

Ahrefs hasn’t really launched anything that is GEO-specific yet, but they provide powerful SEO tools crucial for building and maintaining site authority, indirectly supporting GEO effectiveness. They’re a key player that we cannot ignore in our research, but they aren’t a true GEO tool yet, so we’ve ranked them at the bottom of the list for now.

• Key Features: Comprehensive backlink analysis, site and content auditing.
• Pros: Excellent for improving overall site credibility.
• Cons: Does not directly track generative AI mentions or visibility.
• Pricing: Starts at $99/month.
• Founded: 2010
• Headquarters: Singapore

Generative Search Optimization Tools: FAQs

Final Thoughts

As AI search continues to grow, leveraging these emerging GEO tools to monitor and optimize your brand visibility will be critical. Choose tools aligned with your business size and goals, focusing on clear metrics and actionable insights. Starting with GEO today ensures your content remains visible, authoritative, and relevant as our world continues to be revolutionized by AI.

The post 9 Best Generative Search Optimization Tools (GEO Tools) for 2025 appeared first on NoGood™: Growth Marketing Agency.

These agents aren’t chatbots, they’re autonomous, task-driven systems trained to understand context, make decisions, and take action. Whether it’s generating SEO content, analyzing campaign performance, tracking brand visibility in AI search, or personalizing customer journeys in real-time, agents are doing the grunt work so humans can do the high-leverage thinking.

In this list, we’ve researched and rounded up the 12 most useful AI agents built for marketers right now, the ones actually driving results across brand, growth, and creative teams. If you want to scale your output without scaling your headcount, this is where to start.

The 12 Best AI Agents for Marketers

1. Goodie AI
2. Agentforce by Salesforce
3. Opal by Optimizely
4. ZBrain AI Agents
5. Relevance AI Agents
6. Botpress
7. Taskade AI Agents
8. Growf
9. Lyzr AI Agents
10. Akira AI
11. Chatsonic by Writesonic
12. Breeze by HubSpot

Goodie AI

Best For: AEO, GEO, Brand Visibility, AI Search Optimization & Multi-Agent Content Operations

Starting here with AI search agents as the shift from search engines to answer engines represents the biggest platform shift marketers seen for years.

Goodie AI is a leading AEO platform designed to help enterprise brands take control of their visibility and reputation across AI search engines and LLMs like ChatGPT, Perplexity, Gemini, Claude, and more.

With Goodie, brands deploy autonomous AI agents that track, analyze, and optimize their presence across AI-generated answers. These agents act as a full-stack operating system for Answer Engine Optimization (AEO), combining real-time monitoring, strategic content recommendations, and smart attribution tools, all in one platform.

Goodie is designed for performance marketers, SEO leaders, and content teams looking to win in a world where AI is the new search front door.

Key Features and Functionalities

• AI Search Monitoring: Track your brand mentions, visibility, sentiment, and share of voice across major AI platforms. See exactly how your brand shows up (or doesn’t) in responses across agents like ChatGPT, Perplexity, Gemini, and more.
• Brand Intelligence Layer: Unify performance data from AI assistants, organic search, and LLM interactions. See how AI search contributes to the funnel — from impressions to pipeline — and compare your performance to key competitors.
• AEO Visibility: Get a proprietary brand score that reflects how visible, discoverable, and trusted your brand is across AI search experiences. Benchmarked against industry competitors.
• Conversation Explorer: Understand exactly how users are prompting LLMs to find products or services in your space. Track the phrasing, intent, and sentiment behind key queries driving brand discovery.
• Agent Analytics & Attribution: See which agents are driving the most impact. Attribute visibility gains and lead conversions directly to AI search and specific optimization efforts.
• Enterprise-Ready Architecture: Built with compliance, security, and scale in mind. Multi-workspace support for agencies and global brand teams. SOC2 in progress.

Pricing

Custom pricing available for mid-market and enterprise teams. Contact Goodie for a demo and tailored plan.

2. Agentforce by Salesforce

Best For: Customer Service & eCommerce Personalization

Agentforce by Salesforce is an AI-powered platform designed to create and manage autonomous agents that enhance various business operations, including marketing. These agents operate 24/7, providing proactive support to both employees and customers by executing tasks autonomously within predefined parameters.

The platform emphasizes customization, including a low-code agent builder where marketers can quickly configure new agents or update existing ones using Salesforce tools and capabilities to create tailored solutions for particular tasks or business needs.

Agentforce now also integrates with Slack (also owned by Salesforce), allowing for seamless collaboration between marketers and their AI agents as they learn from and incorporate conversational data.

Key Features and Functionalities

• End-to-End Campaign Management: Agentforce assists marketers in building comprehensive campaigns by generating briefs, targeting audience segments, creating content, and constructing customer journeys.
• Continuous Performance Analysis: The agents monitor campaign performance against key performance indicators (KPIs) and proactively recommend improvements to enhance effectiveness.
• Personal Shopper Functionality: Agentforce can engage customers as personal stylists, offering personalized product recommendations and assisting with search queries directly on commerce sites or through preferred messaging apps like WhatsApp.
• Seamless Data Access: Agents have access to trusted business data, including CRM information, ensuring that responses and actions are grounded in accurate and relevant information.
• Defined Guardrails: Users can set natural-language instructions to establish what agents can and cannot do, including guidelines for escalating tasks to human employees when necessary.
• Slack Integration: Agents can handle busy work, answer questions, and deliver instant expertise, allowing teams to focus on high-impact tasks.

Pricing

3. Opal by Optimizely

Best For: Content Generation & Project Management

Opal by Optimizely is an AI-powered assistant embedded within the Optimizely One platform, designed to enhance marketing workflows through intelligent automation and data-driven insights. Leveraging advanced AI models, Opal assists marketers in various tasks, from content creation to project management, ensuring efficiency and brand consistency.

As an AI agent, Opal can process and understand complex information to understand what to do next or provide recommendations that are personalized to you and/or your business. Marketers can provide Opal with custom instructions, brand information, and any other user-specific preferences that it can learn from to perform better.

Opal can be used for a variety of specific use cases, and Optimizely has also partnered with Microsoft and Google to continue working on AI development that will further improve the digital experience.

Key Features and Functionalities

• Campaign Brief Ideation: Opal references past performance data to suggest topics and define objectives, aiding in the creation of effective campaign briefs.
• Branded Content Creation: Opal generates content that aligns with your brand’s voice, ensuring consistency across all materials.
• Auto-Task Generation: Opal can anticipate tasks for campaigns and projects, streamlining project management by organizing and assigning tasks to team members.
• Task Tracking: It monitors deadlines and priorities, ensuring that projects stay on schedule and team members are informed of upcoming responsibilities.
• Suggested Audience Segments: Opal recommends and generates audience segments to enhance the precision of targeted campaigns.

Pricing

Pricing per plan available upon request.

4. ZBrain AI Agents

Best For: Highly Specialized Tasks

ZBrain offers a suite of AI agents designed to enhance marketing operations by automating tasks, providing data-driven insights, and streamlining workflows. These agents assist in various marketing functions, enabling teams to focus on strategic initiatives and improve overall efficiency.

With ZBrain, you can coordinate multiple different AI agents at a time to support tasks across business functions. ZBrain’s agents integrate with many existing tools and platforms to ease collaboration. They can also learn from human feedback, improving their execution on marketing tasks from end to end, and communicate amongst themselves to coordinate task completion.

Key Features and Functionalities

• Blog Topic Generation Agent: Suggests trending and audience-relevant topics to inspire engaging content creation.
• Content Development Agent: Automates the creation of SEO-optimized content, including blogs, articles, social media posts, and product copywriting.
• Backlink Analysis Agent: Evaluates backlink quality and provides strategies for acquiring high-quality links, enhancing SEO rankings to improve online visibility.
• Off-Page SEO Agent: Suggests and monitors high-quality backlinks to boost visibility.
• Email Campaign Personalization Agent: Crafts personalized email campaigns tailored to segmented audience groups to drive engagement and conversions.
• Market Research Summarization Agent: Summarizes complex market data into actionable insights to guide strategic decisions.
• Competitor News Aggregation Agent: Aggregates and summarizes competitor news to enhance competitive intelligence and strategic insights.
• Social Media Content Generator Agent: Generates engaging social media content to boost online presence and drive higher engagement.
• Social Media Trend Monitoring Agent: Tracks and analyzes social media to spot emerging consumer trends, aiding marketing teams in adapting strategies effectively.
• Customer Feedback Sentiment Analysis Agent: Analyzes customer feedback across channels to identify sentiment, helping enhance products and customer experiences.
• Press Release Drafting Agent: Automates and streamlines press release drafting for timely delivery and efficient media relations.

Pricing

Contact ZBrain for demo and pricing.

5. Relevance AI Agents

Best For: Building Custom Agents from a Manageable Template Library

Relevance AI provides a platform for building and deploying AI agents tailored to enhance and automate marketing operations. Based on the AI agent(s) you build, they can assist in research to help you better understand your customers, content creation, and personalized lead nurture development.

Relevance AI works on a no-code builder, so you can create and refine your AI agents through simple, structured LLM prompts. The agents also integrate easily with common marketing platforms like HubSpot and Salesforce, so they can easily become a part of and start improving your existing workflows.

They do provide a library of existing tools and templates to help you get started building your AI agent(s).

Key Features and Functionalities

• Customer Understanding: These AI agents can swiftly summarize insights from various sources, such as industry reports and user interviews, to provide a comprehensive understanding of customers and prospects.
• Unique Content Development: Agents assist in creating content that aligns with a brand’s data, style, research, and guidelines.
• Personalized Nurture Sequences: AI agents develop tailored nurture sequences to engage leads effectively, preventing them from becoming inactive and enhancing conversion rates.
• At-Risk Customer Identification: Agents identify customers who are at risk of disengaging and provide strategies to re-engage them, thereby maximizing the value of marketing campaigns.
• No-Code Customization: Marketers can easily teach, train, and customize AI agents without coding, allowing for seamless integration into existing workflows.
• Tech Stack Integration: Agents can be integrated with various tools and platforms, such as Zapier and Snowflake, facilitating smooth onboarding.

Pricing

6. Botpress

Best For: Platform Integrations & Natural Language Processing

Botpress is a versatile AI platform that enables businesses to create and deploy AI agents tailored for various applications, including marketing. These agents are designed to optimize marketing strategies by supporting testing and optimization of campaigns, accurately segmenting audiences, and identifying optimal content types and timing to enhance customer engagement.

The AI agents from Botpress can help eliminate menial tasks, like data capture, or enhance personalization through tailored emails and messaging. Plus, they can be built in an intuitive visual design studio, making it easy to create an agent that will directly fulfill what you need.

As a comprehensive platform, Botpress also has functionalities that enable you to test the AI agents you’ve built, manage their knowledge and activities, and connect them with critical third-party marketing tools like Google Analytics and HubSpot.

Key Features and Functionalities

• Natural Language Understanding (NLU): Botpress’s NLU capabilities allow AI agents to comprehend and process user inputs effectively to facilitate meaningful interactions.
• Large Language Model (LLM) Integration: The platform integrates with multiple modes of LLMs to enhance the depth and relevance of conversations, ensuring users receive accurate and contextually appropriate responses.
• Language Flexibility: Botpress supports multiple languages, so marketers can reach a diverse global audience.
• Platform Integration: AI agents can be deployed across various channels, including websites, social media platforms, and messaging apps.
• CRM and Analytics Integration: Botpress AI agents seamlessly connect with Customer Relationship Management (CRM) systems and analytics platforms, providing marketers with valuable insights into customer behavior and campaign performance.
• Automated Engagement: AI agents can initiate conversations with website visitors, qualify leads by asking pertinent questions, and schedule meetings, thereby streamlining the lead generation process.
• Dynamic Content Generation: By analyzing user data, AI agents can generate personalized email and messaging campaigns tailored to individual preferences and behaviors, enhancing engagement and conversion rates.

Pricing

7. Taskade AI Agents

Best For: Extensive Library of Pre-Built Agents

Taskade offers a suite of AI-powered agents designed to enhance marketing workflows by automating tasks, generating content, and providing data-driven insights. These agents are customizable to align with specific marketing objectives.

Taskade provides pre-built agents that can do anything from craft an entire content marketing plan to schedule tasks, forecast sales, or post on specific social media apps. The platform also enables users to build additional, custom agents to meet needs not captured by the existing library.

Once you’ve built a custom agent, you train it with personalized knowledge that you choose to shape it for its intended task. If you don’t want to create the AI agent from scratch yourself, you can also use Taskade’s AI Agent Generator, which allows you to describe what you need the AI agent to do, and the generator will finish the task of creating the functioning agent.

Key Features and Functionalities

• Workflow Generation: Use agents to generate projects, instructions documents, mind maps, or task lists.
• Multiple Agent Teams: Create specialized teams of multiple AI agents to interact with several agents simultaneously and pull their separate functionalities together to complete a project.
• Custom Agent Training Knowledge: Have full control over the knowledge – documents, files, web resources, etc. – used to train your custom agents.
• Autonomous AI Agents: With access to GPT 4o, build and train AI agents that can function on their own, completing necessary research and tasks to inform your strategies.

Pricing

8. Growf

Best For: B2B Marketing Needs

Growf is an AI-powered B2B marketing platform designed to streamline and enhance marketing strategies by automating key processes and providing data-driven insights. It serves as a full-service marketing consultant, enabling businesses to efficiently manage audience research, content creation, SEO optimization, and advertising campaigns.

Growf enables marketers to utilize AI to both set up and execute on their strategies. While it provides functionalities for creating ideal customer profiles, finding potential customers, and communicating key product features, it also can summarize audience research to help identify keywords, craft content, generate campaign ideas, and analyze those campaigns. It’s truly built as a comprehensive and immersive platform.

Key Features and Functionalities

• Ideal Customer Profiles (ICPs): Growf enables marketers to create detailed ICPs swiftly by analyzing existing customer data to facilitate a deeper understanding of target audiences.
• Buyer Persona Development: The platform generates comprehensive buyer personas by analyzing job titles and decision-making roles within target companies.
• Value Proposition Crafting: Growf assists in transforming product features into compelling value propositions tailored to resonate with specific buyer personas, enhancing messaging effectiveness.
• SEO and Keyword Research: The platform automates keyword research by leveraging audience insights to generate targeted keywords.
• SEO-Optimized Content: Growf produces engaging, SEO-friendly content tailored to selected audiences and keywords.
• Campaign Ideation: The platform offers creative campaign ideas aligned with marketing objectives, reducing the time spent on brainstorming and enhancing strategic planning.
• LinkedIn Advertising Management: Growf streamlines the creation, analysis, and optimization of LinkedIn advertising campaigns to enable precise targeting and effective budget utilization.
• Campaign Analysis and Optimization: The platform provides real-time metrics and performance tracking, allowing marketers to test, refine, and optimize their campaigns for better results.

Pricing

9. Lyzr AI Agents

Best For: Deployment Flexibility

Lyzr offers a suite of AI-powered agents designed to enhance marketing operations by automating tasks, generating content, and providing actionable insights. These agents assist marketers in streamlining workflows, improving engagement, and optimizing campaign performance. In addition to their pre-built suite of marketing AI agents, you can also design your own.

While Lyzr provides a platform for both building and using your AI agents, they do offer flexibility in exact deployment methods. You have the option to use your agents through Lyzr’s Agent Studio or through your own infrastructure. With Lyzr’s SaaS platform, agents are set up instantly, any updates happen automatically, and the platform is monitored 24/7. Even if you choose to deploy your agents locally on your own infrastructure, you’ll still have dedicated support from the Lyzr team.

Key Features and Functionalities

• Automated Scheduling and Execution of Campaigns: Manages multi-channel marketing campaigns by automating the scheduling and execution of marketing initiatives.
• Performance Tracking: Monitors campaign metrics in real-time, providing insights to optimize strategies for better results.
• Audience Personalization: Tailors campaigns to specific audience segments for enhanced relevance and engagement.
• SEO-Optimized Content Creation: Generates engaging blogs, newsletters, and ad copies that are optimized for search engines.
• Brand Consistency: Maintains a consistent brand tone across all content types.
• Brand Mention Monitoring: Tracks online mentions of the brand across various platforms to gauge public perception.
• Trend Analysis: Identifies emerging trends related to the brand or industry, providing opportunities for timely engagement.
• Multi-Platform Adaptation: Generates ad content suitable for various platforms.
• Automated Posting: Schedules and publishes posts across multiple social media platforms for a consistent online presence.
• Competitor Strategy Monitoring: Keeps track of competitor marketing strategies in real-time.

Pricing

10. Akira AI

Best For: Workflow Automation & Predictive Reporting

Akira AI is a platform that offers a suite of AI-powered agents designed to enhance marketing operations by automating tasks, providing data-driven insights, and personalizing customer interactions. These agents assist marketers in streamlining workflows, optimizing campaigns, and improving overall marketing effectiveness.

Using natural language prompts, you can guide the creation of automated workflows that are simple to deploy and immediately optimize your operational processes. The AI agent hub also provides access to a collection of pre-built agents that you can start using with just a few clicks. These agents can also be integrated into any of your existing workflows.

Key Features and Functionalities

• Automated Campaign Workflows: Akira AI agents automate the creation, scheduling, and optimization of marketing campaigns across various channels, including email, social media, and pay-per-click (PPC) advertising.
• Advanced Customer Segmentation: Utilizing AI-driven insights, the agents analyze customer data and behaviors to create detailed audience segments. This enables marketers to design hyper-targeted campaigns that resonate with specific customer groups.
• Personalized Content Delivery: Based on user preferences, location, behavior, and interaction history, Akira AI agents dynamically tailor content to provide each customer with a personalized journey.
• Data-Driven Reporting and Analytics: The agents offer real-time insights into campaign performance across multiple channels, highlighting key trends and areas for improvement. Automated reporting provides actionable data, facilitating informed decision-making.
• Predictive Analytics: Employing machine learning, Akira AI agents predict future marketing trends and customer behaviors, allowing teams to anticipate market shifts and adjust their strategies proactively.

Pricing

Contact Akira AI to request a demo and understand pricing.

11. Chatsonic

Best For: Content Generation & Advanced Integration Needs

Chatsonic by Writesonic is an AI-powered marketing assistant designed to streamline and enhance various aspects of marketing workflows. By integrating advanced AI models with essential marketing tools, Chatsonic assists marketers in research, content creation, optimization, and publication, ensuring efficiency and brand consistency.

Chatsonic utilizes and allows users to switch between multiple AI models, including GPT-4, Claude, and Gemini, enabling marketers to use the tool that may be best suited to their needs at any given time.

This AI agent can also be connected to platforms like Ahrefs, Google Search Console, and WordPress to further facilitate the data gathering and content production process.

Although set up as a chat interface, Chatsonic goes beyond a basic chatbot. Marketers can use the platform for the vast majority of tasks throughout a variety of workflows, including researching, writing, creating reports, and coding. Chatsonic can process specific brand voice and style guidelines to ensure generated content is in alignment and consistent across channels.

Key Features and Functionalities

• Marketing Tool and Platform Integration: Chatsonic connects with platforms like Ahrefs, WordPress, Hubspot, and Google Analytics so you can get real-time data without leaving the chat interface.
• Customizable Brand Voice and Style: Marketers can define and maintain their brand’s unique tone and messaging across all content types, ensuring consistency in every interaction.
• Enhanced Collaboration Tools: Features like Canvas and Artifacts facilitate collaborative writing and coding projects, allowing teams to work together seamlessly beyond simple AI chat interactions.
• Up-to-Date Information Gathering: Chatsonic provides real-time web search capabilities, ensuring that content and strategies are aligned with the latest industry trends and data.
• Custom AI-Powered Processes: Marketers can create custom workflows for content creation, SEO optimization, and campaign management, streamlining processes from ideation to publication.

Pricing

12. Breeze by HubSpot

Best For: Virtual Assistance & Automated Workflows

Breeze is HubSpot’s comprehensive AI suite designed to enhance marketing efforts by automating tasks, generating insights, and personalizing customer interactions. It integrates seamlessly into HubSpot’s platform, providing marketers with tools to improve efficiency and effectiveness.

Breeze includes both Breeze Copilot and Breeze Agents. Breeze Copilot is an easy-to-use AI assistant that can assist you with any task within HubSpot’s platform. It understands your CRM data and uses generative AI to help you execute your tasks quickly.

Breeze Agents are “experts” in specific tasks and can help you build automated workflows, from planning stages all the way through execution. The Breeze Agents are currently in Beta.

Key Features and Functionalities

• Content Creation: Generates high-quality content like landing pages, podcasts, blogs, and case studies, tailored to the brand’s voice and informed by CRM data.
• Task Automation: Streamlines repetitive tasks such as scheduling posts and managing email campaigns.
• Data Summarization: Provides concise summaries of CRM records and marketing analytics, facilitating quick decision-making.
• Social Media Management: Analyzes the brand’s social presence and creates posts using company details, audience insights, industry trends, and marketing best practices.
• Prospecting: Researches and executes personalized outreach strategies for prospects within HubSpot’s Smart CRM.
• Data Enrichment: Automatically populates contact and company records with up-to-date information, improving segmentation and targeting.
• Buyer Intent Identification: Detects which companies are showing interest in your website, enabling proactive engagement strategies.

Pricing

The future of marketing is agentic!

The integration of AI agents into marketing strategies is no longer a futuristic concept but a present-day necessity. These advanced tools empower marketers to automate repetitive tasks, gain deeper insights into consumer behavior, and deliver personalized experiences at scale.

By leveraging the capabilities of AI agents, businesses can enhance efficiency, optimize campaigns, and achieve superior outcomes in an increasingly competitive marketplace. Embracing AI-driven solutions will be pivotal for marketers aiming to stay ahead of the curve and foster meaningful connections with their audiences.

FAQs

The post Top 12 AI Agents for Marketers in 2025 appeared first on NoGood™: Growth Marketing Agency.

While creating content for AEO is becoming more manageable, the bigger challenge lies in answering one crucial question: How do you measure the performance of your AEO efforts? Unlike traditional SEO, where rankings and traffic data are straightforward and consistent, AEO operates in a probabilistic space—the same question might yield different answers across different AI platforms, or even from the same model. These answers are often non-clickable, making performance tracking less direct and far more nuanced.

In this blog, we will break down exactly how to track and evaluate your AEO initiatives—what to measure, why it matters, and how to do it well.

AEO vs SEO Goals

With AEO, the goal isn’t necessarily to drive instant traffic or direct conversions. Instead, it’s about brand discovery and presence within AI-generated responses.

Why is this important? Because users often don’t go beyond the initial AI-generated answer. There’s no need to click a link, scroll through pages, or open multiple tabs. The AI delivers the entire response in one go. And yet, this interaction can significantly influence user behavior.

Even without a click, an AI-generated brand mention:

• Builds brand awareness
• Establishes credibility through AI’s “endorsed” answer
• Influences future searches, conversations, and buying decisions

Example: If your brand is listed in ChatGPT as a top tool in your industry, that mention may not lead to immediate traffic, but it increases the chances of users later searching for your brand, visiting your site, or converting through another channel.

Why don’t traditional SEO metrics apply to AEO? In SEO, rankings, impressions, and click-through rates provide a clear map of performance. These are based on deterministic systems—if your site ranks #1 today, chances are it still ranks #1 tomorrow, unless something significantly changes.

AEO doesn’t work like that. LLMs rely on probabilistic models, meaning:

• Responses vary based on the model’s training and external web sources
• Results aren’t fixed or consistent
• Answers may not include clickable links at all

That’s why AEO performance must be evaluated differently. You’re not tracking just traffic or rankings—you’re measuring presence, prominence, sentiment, and how those influence user behavior over time.

What AEO Performance Metrics Should You Track?

Here are 3 types of metrics that can help you evaluate AEO performance meaningfully:

1. Visibility Metrics (Primary metric)

Visibility is the foundation of AEO. These metrics help you assess how often and how prominently your brand is being included in AI-generated answers.

• Brand visibility score in ChatGPT, Gemini, Perplexity, etc: This reflects how frequently your brand is mentioned in AI responses relevant to your category. For example, if you ask Gemini, “What’s the best task management tool for teams?” and your brand is listed in the top three, that’s a win, even if it doesn’t lead to a click.
• Position or priority in LLM answers: Are you mentioned first, last, or somewhere in the middle? Higher placement often reflects stronger brand authority. Being listed in a response should be considered a win.
• Sentiment in brand mentions: LLMs learn from online content, including reviews (e.g., G2, Capterra), discussions (e.g., Reddit), and articles. Positive sentiment in these sources makes favorable mentions more likely in AI answers.

2. Engagement Metrics

Much of the brand discovery and evaluation happens within the AI interface itself, before a user ever visits your website. As a result, while your overall website traffic may decrease, traffic quality is likely to improve. Users who do click through have already done a fair amount of research through AI and are coming in more informed, curious, and ready to engage. This often translates to higher-quality sessions on your site.

To measure this effectively, focus on the following engagement metrics:

• Bounce rate: Are visitors sticking around or leaving right away?
• Average time on site: Are they exploring your content in depth?
• Pages per session: Are they engaging across multiple pages?

Engagement metrics tend to improve when your brand’s visibility and positive sentiment for the brand increase. For example, if Nike sees a rise in brand visibility for “best running shoes” across AI searches, they should also expect improved engagement on their running shoe product pages—across all traffic channels, including organic, direct, and paid.

3. Conversion Metrics

Although AEO primarily plays a role at the top of the funnel, it’s important to track its impact on downstream conversions. You may have noticed that many AI platforms—like ChatGPT—are now starting to appear in your website analytics as referral sources, often tagged with UTM parameters such as utm_source=chatgpt.com. As traffic from these LLM-powered platforms grows, you may begin to see a lift in bottom-of-funnel metrics, including:

• Actions initiated (form submissions, demo requests, or product views)
• Actions completed (lead forms submitted, purchases made, or demos scheduled)
• Live chat engagements
• Product support inquiries
• Direct revenue generated

The Challenge of Measuring AEO: Rag Is a Strong Influencer of Performance

Retrieval-Augmented Generation (RAG) is a technique used by most leading LLMs to enhance the quality and relevance of their responses. RAG-enabled AI models pull in real-time information from the web to supplement their answers.

Because these models reference external sources dynamically, any changes in the availability, credibility, or ranking of your brand across these sources can directly influence the outcome of an AI-generated response, similar to how Google’s algorithm updates shift the weight given to certain websites over others.

RAG allows LLMs to:

• Actively retrieve live data from the internet at the time of the query
• Combine it with their existing training knowledge
• Generate fresh, contextually rich responses using high-authority sources

Example: Ask Perplexity, “What are the top CRM tools for nonprofits?” and it might reference current blog posts, software comparison sites, and industry articles to compile its answer. If your brand is mentioned frequently in those sources, it increases your chances of being included.

Manual vs. Automated AEO Performance Tracking

When it comes to tracking your brand’s presence in AI search, there are two primary approaches: manual audits and automated tools. Unlike the Google search ecosystem, where we have tools like Keyword Planner, SEMRush, or Ahrefs to help us with search performance over time, similar reporting metrics are still a black box for the AI search ecosystem.

1. Manual Tracking: Hands-On Learning

Manual tracking typically involves running periodic queries across AI platforms like ChatGPT or Gemini to observe how and where your brand appears. This includes:

• Asking the same set of queries each week to check for consistency
• Noting any changes in phrasing, ranking, or brand mentions
• Reviewing the sentiment and context in which your brand is mentioned

Example: Maintain a simple spreadsheet tracking whether your brand shows up for queries like “best CRM for small businesses” or “top productivity tools for remote teams” every week across key AI search platforms.

While manual audits can be time-consuming, they offer significant value in today’s rapidly evolving AI landscape. These hands-on checks provide deeper insight into how different models interpret and represent your brand—nuances that automated tools may still miss. Though not easily scalable, manual analysis remains a powerful complement to automation and should be part of your routine, even if you already use other tracking tools.

2. Automated Tracking: Scalable & Consistent Monitoring

As the space matures, tools like Goodie AI have emerged to help brands monitor their AI search performance at scale. These platforms offer features such as:

• Brand visibility scores across ChatGPT, Gemini, Perplexity, and others
• Sentiment and positioning analysis
• Competitor benchmarking
• Topic-level reporting tied to your business objectives
• Optimization suggestions to improve inclusion and prominence

Because AEO is about visibility rather than traffic, these tools focus on discoverability metrics—measuring how often, and how favorably, you appear in AI-generated answers.

Goodie in Action: AEO Power Tool for Modern Brands

Goodie AI is a leading platform built specifically for AEO tracking and optimization. It offers a full suite of features to support both measurement and improvement:

• Real-time tracking of brand mentions across AI search engines
• Sentiment analysis across LLMs like ChatGPT, Gemini, Claude, Perplexity, and Deepseek
• Dual optimization strategies for both AI and traditional SEO
• AI-driven content recommendations to close visibility gaps
• Traffic and conversion attribution from AI interactions

💡 Why it matters: In a probabilistic AI environment where exposure = influence, tools like Goodie AI helps you track what matters—and take action to stay ahead.

As AI-driven search continues to evolve, the brands that adapt early—and measure smartly—will lead the way. Traditional SEO metrics no longer tell the whole story. AEO demands new tools, new KPIs, and a new mindset.

Your brand doesn’t need to “rank”—it needs to be recognized, recommended, and remembered. Tracking visibility, sentiment, and brand presence across LLMs is no longer a “nice to have”—it’s a competitive necessity.

The post How to Measure Performance of Answer Engine Optimization (AEO) appeared first on NoGood™: Growth Marketing Agency.

Far more advanced than the chatbots of yesterday, conversational AI uses natural language processing (NLP) and machine learning (ML) to deliver intelligent, human-like interactions at scale. From streamlining operations and boosting patient engagement to enabling scalable personalization, it’s quickly becoming a game-changer in modern healthcare.

We’ll explore the benefits, use cases, and strategic opportunities conversational AI brings to healthcare, along with the challenges and practical steps to get started.

It’s Not Just Chatbots Anymore, It’s a New Patient Experience

Leveraging conversational AI in healthcare applications means evolving from traditional chatbots. Traditional static chatbots follow a predefined script. While they can assist users in simple tasks such as appointment reminders, scheduling, and directing users to different website pages based on queries, they’re limited to those interactions.

But conversational AI learns and adapts. They can process complex queries and understand nuances in patient language.

Conversational AI is smarter than traditional chat systems and healthcare applications, meaning patients can describe their symptoms as if they’re talking to a nurse. Then, they’ll receive informed, contextually relevant responses that consider medical history, medications, and even lifestyle changes.

This delivers meaningful patient support and reduces reliance on symptom-checking through generic search engines, where worst-case scenarios often top the results. Instead of fielding anxiety like the all-too-familiar “WebMD told me I’m dying” meme, conversational AI can offer grounded, personalized guidance that builds trust and encourages more proactive care.

Additionally, conversational AI systems are designed to integrate seamlessly into existing healthcare infrastructures. They work in tandem with electronic health records (EHRs), patient management systems, and other digital tools to ensure that the information provided is accurate and up-to-date.

The Marketing Opportunity Behind Smarter Patient Conversations

Conversational AI isn’t just a clinical tool, it’s a brand experience engine. For healthcare marketers, it delivers 4 key benefits that transform how you connect with and support patients: 

• Scalable Personalization: Deliver tailored content, guidance, and support to thousands of patients, automatically and empathetically.
• Omnichannel Consistency: Extend your brand voice across chat, SMS, voice, and app interfaces, meeting patients where they are.
• Data-Driven Insights: Capture rich, first-party data from real-time interactions to optimize the patient journey.
• Loyalty Through Empathy: Build deeper patient trust through responsive, human-like interactions that feel genuinely helpful.

Benefits of Leveraging Conversational AI in Healthcare

Enhancing Patient Care

Conversational AI offers healthcare organizations a way to enhance patient care and operational efficiency, the most significant advantage being improved patient engagement. Through personalized interactions, meaningful communication between patients and healthcare providers can be facilitated. This helps boost patient satisfaction and encourages users to be active participants in their health management.

Streamlining Operations

Streamlining operations is another major benefit of using conversational AI within healthcare platforms. Conversational AI can be programmed to automate various administrative tasks, from appointment scheduling to prescription refills, drastically reducing the burden on healthcare staff. This automation frees up valuable human resources, allowing them to focus on more complex patient care tasks. Moreover, it minimizes the risk of human error in routine processes, ensuring smoother and more reliable administrative operations.

Cost Efficiency

Cost reduction is another compelling advantage that cannot be overlooked. Minimizing the need for human intervention in routine inquiries and tasks can significantly cut operational costs. Resource allocation becomes more efficient, allowing for better utilization of both human and technological assets. This cost-effectiveness is particularly beneficial for smaller healthcare providers operating with tighter budgets.

Accessibility

Accessibility to care is greatly enhanced with conversational AI. These systems can provide 24/7 support, ensuring patients can access necessary information and assistance whenever they need. This round-the-clock availability is critical for patients who may require immediate answers or support outside of regular office hours. Additionally, data-driven insights garnered from AI interactions empower healthcare providers to gain a deeper understanding of patient needs and behaviors, leading to more informed decision-making.

Improved Patient Satisfaction

Conversational AI enhances patient satisfaction by making care more accessible, personalized, and empathetic. It provides real-time support across chat, voice, and SMS, reducing wait times and improving convenience. Unlike static chatbots, it adapts to patient needs, remembers past interactions, and delivers warm, human-like responses.

By streamlining communication, minimizing errors, and proactively offering guidance, AI helps patients feel supported, informed, and cared for every step of the way.

Scalability

Another impactful advantage of conversation AI in healthcare is its scalability. Unlike human staff, AI can handle thousands of patient interactions simultaneously without compromising quality or consistency. This makes it possible for healthcare organizations to extend support beyond normal business hours, manage high volumes during peak times, and serve growing patient populations without a proportional increase in overhead.

Omnichannel Consistency

Today’s patients interact with healthcare companies across multiple touchpoints, including the web, mobile apps, SMS, voice assistants, and more. Conversational AI assists in maintaining a unified brand experience across all of these channels. Whether someone asks a question via live chat or calls into a voice assistant, the tone, quality, and accuracy of the response can be consistent. That builds trust and ensures patients always know what to expect, no matter how they reach out.

Data-Driven Insights

Every interaction with conversational AI becomes a valuable data point. Marketing teams can mine these conversations for real-time insights into patient needs, concerns, and behavior. This first-party data helps refine messaging and journey mapping while supporting compliance-conscious audience segmentation and campaign personalization without relying on third-party cookies or invasive tracking.

Loyalty Through Empathy

Conversational AI doesn’t just respond, it listens. Natural language understanding allows it to pick up on tone, urgency, and intent, creating a more empathetic experience that resonates with patients. When people feel heard and helped, not just processed, they’re more likely to stay loyal to the healthcare company, recommend it to others, and consistently engage over time.

Challenges of Implementing Conversational AI in Healthcare

As is the case with most cutting-edge technologies, implementing conversational AI in healthcare isn’t without its hurdles. Scaling it effectively requires thoughtful planning and cross-functional alignment. But whether you’re goal is to improve patient satisfaction, increase operational scalability, or strengthen your brand experience, understanding these key challenges upfront is key to long-term success:

Privacy & HIPAA Compliance

Privacy concerns loom large. With sensitive patient information at stake, ensuring HIPAA compliance is non-negotiable. This involves implementing stringent data protection measures to safeguard patient confidentiality while still allowing for the AI’s robust capabilities.

Continuous Refinement for Clinical Accuracy

AI systems, while advanced, are not infallible. Misinterpretations or errors in responses can occur, highlighting the need for continuous refinement. Regular updates and oversight are critical to maintaining the AI’s reliability and accuracy, ensuring that patient interactions remain effective and trustworthy.

Integration with Legacy Systems & Clinical Workflows

Integration poses another significant hurdle. Many healthcare systems rely on legacy software and processes, making seamless interoperability a daunting task. Bridging these technological gaps requires strategic planning and substantial investment to ensure new AI solutions can communicate effectively with existing systems, thus preventing disruptions in service delivery.

Upfront Investment vs. Long-Term ROI

While conversational AI can help you save money in the long run, initial investment, along with the resources required for ongoing management and improvement, can be substantial. For many healthcare providers, especially smaller practices, finding the budget for such an undertaking demands a clear demonstration of return on investment. This can make conversational AI an unrealistic avenue for smaller healthcare organizations.

However, options like modular deployments, partnerships with AI vendors offering flexible pricing, or starting with narrowly scoped pilot programs can help smaller practices test the waters without committing to a full-scale implementation upfront.

How to Get Started with Conversational AI

Implementing conversational AI doesn’t have to be a massive transformation project from day one. The most successful initiatives often start small by targeting key patient touchpoints and scaling based on impact. Here are some key (but accessible) places to start:

Map Your Patient Journey

Start by identifying all the touchpoints where patients currently interact with your organization, paying special attention to areas where friction is the highest. Think of your patient journey as a funnel– at which point do you typically experience the largest drop-off in engagement or follow-through? Are patients abandoning the process during scheduling, or failing to follow yo after a visit?

If scheduling or follow-up requires a phone call or puts too much burden on the patient, that’s a clear opportunity to introduce conversational AI. Automating these interactions can reduce friction, streamline access to care, and eliminate the need for patients to wait on hold or navigate complex phone trees.

Another area you can derive insights from is looking at where staff are overwhelmed with repeat questions. Are they spending a lot of time providing flu vaccine information? How about calling to get appointment confirmations? These are areas where you can benefit both your employees and patients.

Additionally, many healthcare providers are limited to their hours of operation, and as we all know, humans don’t get sick on a schedule. Are there moments that can be improved with 24/7 support? Things to consider can include pre-visit touchpoints like billing and insurance questions, during-care touchpoints like real-time frequently asked questions and medication guidance, or preventative care touchpoints such as health education and mental health check-ins.

Start with a Focused Pilot

When you’re just starting out on your conversational AI journey, it’s helpful to choose one specific use case that’s easy to measure and offers visible impact. This approach allows you to quickly quantify ROI, build internal momentum, and minimize risk before pursuing a broader rollout.

Some examples of first pilots include automating appointment scheduling and reminders via chat, using AI to deliver post-visit satisfaction surveys, or deploying chat-based symptom checkers that escalate to telehealth when needed.

Collaborate Across Teams

Conversational AI touches more than just marketing and patient services—it impacts operations, IT, patient services, and even compliance. During your conversational AI implementation planning, bring key stakeholders in early. Participants in the process must include IT and Data, Clinical Operations, and Legal & Compliance. Framing this as a brand and experience initiative rather than just “a tech thing” makes alignment easier and ensures you get the highest buy-in.

Define Success Metrics and Track Relentlessly

The magic of AI is that it’s highly measurable. Choose KPIs that align with both business goals and patient experience. Key metrics can vary based on business goals, with a few common ones including:

• Engagement Rate: How many patients use the AI feature. Track via analytics dashboards embedded in your chat or voice platform to monitor usage volume and frequency.
• No-show Reduction: Measure appointment completion rates before and after implementing automated reminders or rescheduling prompts.
• Reduced Call Center Volume: Compare call logs and ticket volume pre- and post-AI implementation to quantify deflected calls.
• Net Promotor Scores (NPS): Survey patients after interactions with AI touchpoints to assess likelihood to recommend.
• Customer Satisfaction (CSAT): Implement a no-obligation rating system after post-care interactions or chatbot sessions.
• Conversion Rates: Appointments booked, forms completed. Use form completion analytics or scheduling tool integrations to track conversion rates.
• Cost-Per-Interaction vs. Traditional Channels: Calculate average cost per AI-based engagement versus call center staffing costs or manual outreach.

Now that you’ve determined the KPIs that are the most relevant and valuable to you, start by setting a baseline, running A/B tests where possible, and reporting often. Early results act as your proof of case, so you can decide if conversational AI is an avenue worth pursuing to scale your business.

Scale Strategically

Once your pilot proves its value, the next step is to scale intelligently, expanding conversational AI’s reach while keeping the patient experience cohesive. Don’t wait for “perfect alignment” to scale; instead, build on your momentum with calculated, brand-aligned growth. Here are some ways to think about expansion:

Expand to New Channels

Patients interact differently depending on age, need, and urgency. Meet them where they are. Some applications can include:

• SMS for appointment reminders or follow-ups
• Voice assistants for accessibility and in-home support
• In-app messaging for connected care journeys

Segment and Personalize

Use AI to adapt the experience by patient type or condition. Ideas to consider include:

• Chronic care patients might receive more proactive check-ins
• Maternity patients might get week-by-week resources
• First-time patients might receive onboarding-style guidance

Integrate With Marketing Automation

Don’t trap conversational AI in a silo. Connect it to your existing CRM, email platform, or patient portal to create seamless journeys across:

• Campaign landing pages
• Follow-up emails
• App notifications
• On-site chats with personalized CTAs

Evaluate Your Platform’s Scalability

As you scale, your tech needs to keep up. Look for AI solutions that offer:

• Open APIs for integration with EHR, CRM, and marketing tools
• Granular analytics to track patient behaviors and optimize flows
• HIPAA and HITRUST compliance to stay ahead of privacy and security standards

Scaling isn’t just about adding more bots, it’s about weaving AI into your brand’s patient experience architecture. Think long-term platform, not short-term plug-in.

Partner with Exper

Partner With Experts

Realistically, most healthcare organizations don’t have in-house AI teams, conversational designers, or compliance-savvy developers just sitting around. And that’s okay. This is where a strategic partner can unlock real momentum.

The right partner can help you identify high-impact use cases, align AI with your brand voice, ensure a seamless integration process, confidently navigate compliance, and help you build a clear narrative with metrics, dashboards, and patient feedback.

The Future of Healthcare Is Conversational

Conversational AI isn’t just another tech trend, it’s a powerful enabler of better patient experiences, smarter operations, and stronger brand trust. Whether it’s answering patient questions at 2 a.m., reducing call center load, or guiding patients through their care journey with empathy and precision, AI-powered tools are reshaping how healthcare is delivered and experienced.

For healthcare organizations looking to stand out, improve outcomes, and meet rising patient expectations, now is the time to invest in intelligent, conversational experiences. Start small, measure impact, and scale thoughtfully, but don’t wait.

The post Conversational AI in Healthcare: Benefits + Use Cases appeared first on NoGood™: Growth Marketing Agency.

This is your blueprint for staying visible, clickable, and trusted across Google’s evolving AI ecosystem.

Ranking Factors for AI Mode & Google Search Experience

How AI Mode Works

AI Mode is powered by a customized version of Gemini 2.5, Google’s most intelligent available model. This model is capable of thinking and reasoning through an in-depth thought process before responding, resulting in enhanced performance and improved accuracy.

• AI Overviews + AI Mode use query fan-out: They break a single question into many sub-queries to find the most relevant, high-quality information across the web.
• Deep Search can generate expert-level, cited reports, favoring multi-source, semantically rich pages.
• Real-time multimodal capabilities: AI Mode integrates live visual search through Project Astra, allowing users to have conversations about what they see using their camera.

Content Strategy for AI Overviews & Deep Search

Google’s shift to AI Overviews and Deep Search fundamentally changes how your content is discovered and cited. Instead of returning a list of links, Google is now using Gemini to interpret intent, break queries into subtasks, scan hundreds of sources, and synthesize a cohesive answer.

This changes your job from “get ranked” to “get cited.”

What Google AI is Optimizing For

1. Relevance and specificity
2. Originality and depth of insight
3. Authoritativeness, especially from niche experts
4. Semantic alignment with related subqueries

How to Optimize Your Content

Write for real human intent: Don’t just target keywords. Reverse-engineer full user prompts and sub-questions using forums like Reddit, Quora, and People Also Ask to understand how people phrase real questions.

Structure for skimming AND synthesis: Use subheadings, bullet points, summaries, and data boxes to make it easy for Google to extract meaningful text chunks.

Go deeper than summary-level advice: Offer frameworks, decision matrices, pricing breakdowns, firsthand experiences, expert quotes, or user-generated insights. Additionally, including expert quotes and firsthand experiences improves your brand authority and expertise.

Answer the follow-up question: If a user asks, “Is creatine good for runners?”, include the science behind it, usage tips, side effects, alternatives, and expert opinion. This way, you can cover both the explicit and the implicit user intents.

Create data-backed, expert-level content: AI Overviews are more likely to link to content with citations, named authors, data sources, and external references.

Technical AEO for AI Mode Retrieval

Technical accessibility is the price of admission. Googlebot and Gemini need to find, access, and interpret your content without friction.

Top 7 Must-Haves

1. Pages return 200 status codes (avoid soft 404s, 3xx redirects, or errors)
2. No robots.txt blocking Googlebot or Google-Extended
3. Meta tags like noindex, nosnippet, or data-nosnippet are used intentionally
4. Consistent canonical tagging to prevent duplicate content conflicts
5. Mobile-first design that loads fast and works across screen sizes
6. Proper use of headers (<h1> to <h3>) to reflect content hierarchy
7. Descriptive titles and meta descriptions that align with user intent and long-tail queries

Bonus Tips for AI performance:

• Semantically structured content with NLP-friendly phrasing
• Longer-tail topic pages that cover a full topic cluster rather than just a single keyword
• Embedded answers to FAQs, comparisons, and pros/cons to help LLMs extract structured information

Structured Data & Multimodal Optimization

AI Mode and Overviews rely heavily on structured data to surface and summarize rich content. As Google adds capabilities like visual live chat, virtual try-ons, and financial data graphs, structured data becomes essential.

Structured Data Best Practices

Structured data implementation provides search engines with the context they need to properly understand and showcase your content. Implement schema.org types that align with your specific business needs, including:

• Product markup for e-commerce sites,
• Service schema for professional offerings,
• Article markup for blog content,
• FAQPage schema for support sections,
• Event markup for upcoming activities,
• LocalBusiness for location-based services
• Recipe schema for food-related content etc.

Quality control is essential once you begin implementing structured data across your site. Validate every markup addition using Google’s Rich Results Test to ensure proper implementation, and maintain strict consistency between your schema content and what visitors actually see on the page. Mismatches can result in search engine trust penalties.

Enhance your credibility signals by including author, datePublished, and publisher information wherever applicable. These elements help establish authority and can positively impact how your content performs in search results.

Tips for Multimodal Search Optimization

• Use original images with descriptive, natural-language alt text
• Compress and size images for fast loading
• Add short-form video explainers or walkthroughs
• Upload video transcripts for better semantic extraction
• Keep Google Merchant Center product data and Business Profile details up-to-date and optimized

This matters more now that AI Mode includes Search Live, which lets users point their camera at an object or visual and ask live follow-up questions.

AI-Generated Content Guidelines & Trust Signals

Using AI to write content is not banned. But using AI to flood Google with thin, low-effort pages is grounds for a penalty under their scaled content abuse policy.

Follow These 4 Key Principles

• Don’t mass-publish similar pages that differ only by city/product/category name
• Add real human context and editing to every AI-assisted piece
• Disclose automation where relevant, such as:
  • Author notes
  • Image metadata (using IPTC DigitalSourceTypeTrainedAlgorithmicMedia)
  • Page footers with transparency disclaimers
• Use generative AI to structure, not replace, human creativity. Think: first draft assistant, not auto-publisher.

Metadata and Markup Hygiene

Every page requires custom <title> and <meta description> tags that accurately represent its content and purpose. Your structured data should be descriptive and consistent across all implementations while staying compliant with search engine policies to avoid penalties.

Enhance your markup with additional context signals where relevant, including isAccessibleForFree, inLanguage, and contentLocation tags. These additional data points help search engines better understand and categorize your content for more precise targeting.

How to Measure Performance Beyond CTR

Clicks are a fading metric in the AI search era. Google says AI Overviews drive “higher-quality clicks,” but volume is down and continuing to decrease

Key Metrics

• Conversion rate (form fills, purchases, subscriptions)
• Session depth (pages per visit)
• Time on site and bounce rate
• Return visits and retention
• Lead quality, not just quantity

Tools

• GA4 + Looker Studio dashboards for engagement segmentation
• Heatmaps (Hotjar, Microsoft Clarity) to analyze content friction
• First-click vs. last-click attribution modeling to evaluate AI-driven journeys
• Server-side tagging to capture more reliable data, especially with AI agents acting on behalf of users
• AEO platforms like Goodie AI to monitor visibility, measure performance, and take actions across AI search platforms

Next-Level Tactics for Brand Visibility

AI Overviews and AI Mode pull from a wider range of sources than classic Search. Your brand’s presence across the entire web determines your visibility.

Become an Entity, Not Just a Website

Ensure you build consistent profiles across Wikipedia, Crunchbase, LinkedIn, GitHub, YouTube, and press articles. Using a well-rounded strategy mitigates the chances of incorrect information being sourced and improves visibility through presence. Connect identities through sameAs schema and keep your About Page structured, semantic, and detailed.

Use Semantic Saturation

Build strong associations between your brand and high-intent phrases by consistently positioning your brand name close to the terms that matter most to your business. Create comprehensive, glossary-style content that defines and explores the terminology you want to own within your industry space.

Structure your content using hub-and-spoke topic clusters that demonstrate your domain expertise to AI systems. This approach helps large language models understand the breadth and depth of your authority across interconnected topics, making you more likely to be referenced as a credible source.

Get Mentioned in Credible Sources

Expand your digital footprint through strategic outreach to high-authority blogs, industry publications, podcasts, and newsletters within your sector. Position yourself as a thought leader by contributing to guest articles or participating in expert roundups that showcase your expertise to new audiences.

Build citation momentum across platforms where professionals gather and share insights, including Quora discussions, relevant Reddit communities, LinkedIn thought leadership posts, and Substack publications. These mentions create the reference signals that AI systems use to assess credibility and authority.

Optimize for How People Ask, Not How They Search

Instead of optimizing for traditional keyword phrases, design landing pages that directly answer the conversational questions users actually ask AI systems.

Rather than targeting “CRM for small SaaS,” create content that responds to natural language queries like “What’s the best CRM for small SaaS startups in 2025?” Apply this same approach across different content types, such as creating pages that answer “How to get verified on Instagram without being famous” instead of targeting “Instagram verification,” or “Compare budget electric SUVs with over 300-mile range” rather than “electric SUV comparison.”

These natural language patterns reflect how users communicate with AI assistants and search for information in an increasingly conversational search environment.

Future-Proofing: Agentic Search & AI Actions

Google AI Mode is not just answering questions. It’s now doing things on behalf of users:

• Booking reservations
• Purchasing tickets
• Comparing SKUs
• Surfacing local availability and pricing

If You Want Your Business to Be Selected by AI Agents

Make sure to keep your Google Business Profile up-to-date. Ensure you use availability, price, and action structured data. Additional best practices to improve selectability are to integrate with booking and ticketing partners such as:

• Resy
• OpenTable
• Vagaro
• Ticketmaster
• StubHub

Lastly, to automate your process and simplify updates, use a public API or structured feeds so Google can access real-time inventory

Anticipate Changes in User and AI Agent Behavior

Prepare for the shift toward conversational commerce, where users make specific requests like “find me a black turtleneck under $50, size M, in stock near me” rather than browsing product categories.

Similarly, LLM-driven agents will handle complex, multi-step tasks such as “book the closest available dentist for Thursday 4 pm, under $150,” requiring businesses to structure their data and booking systems for AI accessibility.

We’re Already in the Future of Search

LLMs are shaping what users see, trust, and act on. To stay visible and drive organic growth, your content must be designed for humans AND for generative engines.

This means shifting from keyword targeting to intent mapping, prioritizing organic conversions over vanity traffic metrics, and creating structured, original, expert-level content that demonstrates clear authority. Treat technical SEO as your foundational layer while embracing structured data and multimedia formats that AI systems can easily parse and reference. Focus on deeper engagement metrics that reflect genuine user value, and prepare for a world where AI agents increasingly determine which brands get discovered and which get overlooked.

The rules of search are being rewritten, but the opportunity is enormous. The brands that adapt fastest, those who earn visibility through value, clarity, and semantic strength, will dominate the AI-first era of discovery.

The post AI Mode Optimization Guide: Strategies for Google AI Search Experiences appeared first on NoGood™: Growth Marketing Agency.