AI Agent Optimization Fundamentals: How ChatGPT and Claude Actually Discover Products

Marcus, VP of Product Marketing at a B2B analytics platform, noticed something strange in their demo requests. When he asked new prospects how they found the company, three said the same thing: "ChatGPT recommended you."

Not Google. Not a referral. ChatGPT.

He asked sales to track it. Over the next month, 12% of their inbound pipeline came from prospects who mentioned AI agents—ChatGPT, Claude, or Perplexity. These weren't casual tire-kickers. They converted at 2x the rate of traditional search traffic.

Marcus had optimized for Google for a decade. Now he needed to optimize for AI agents. Different game, different rules.

Why AI Agent Optimization Matters Now

In 2025, AI agents aren't future tech—they're how people research products. ChatGPT has 200M+ weekly users. Claude handles millions of queries daily. Perplexity is growing as the answer engine for professionals.

When someone asks "What's the best analytics platform for B2B SaaS companies?", the AI agent doesn't show ten blue links. It recommends 2-3 specific products with reasoning. If you're not one of those recommendations, you don't exist.

Traditional SEO optimized for ranking on page one. AI agent optimization is about being the recommendation. It's winner-take-most, not winner-take-all, but the stakes are higher.

How AI Agents Actually Discover Products

Marcus spent two months reverse-engineering how ChatGPT and Claude found and recommended products. Here's what he learned.

Discovery Method 1: Training Data

AI models were trained on massive internet datasets scraped before their knowledge cutoff. For ChatGPT-4, that's data through April 2023. For Claude, through early 2024.

If your product had strong web presence before these cutoffs—published content, customer reviews, third-party mentions, technical documentation—the model knows about you. If you launched after, it doesn't unless it searches the web.

This is why older, established products get recommended more frequently. They're baked into the training data.

Discovery Method 2: Web Search

When AI agents don't have information in training data, they search the web in real-time. ChatGPT uses Bing search. Perplexity has its own search engine. Claude can search when enabled.

This is where traditional SEO still matters, but with a twist. The AI isn't reading your entire website. It's scanning search results, pulling snippets, and synthesizing answers. You need to make it easy for AI to extract key facts.

Discovery Method 3: User Context

The query context matters enormously. "Best analytics platform" gets generic answers. "Best analytics platform for B2B SaaS companies with $20M ARR focused on product-led growth" gets specific recommendations.

AI agents use the user's context—industry, company size, use case, constraints—to filter recommendations. This means your positioning needs to be crisp and searchable.

Discovery Method 4: Recency Signals

AI agents prioritize recent, updated information. A blog post from 2020 carries less weight than one from 2025. Documentation that's clearly maintained signals an active, current product.

This is why "last updated" timestamps and fresh content matter for AI visibility.

The AI Agent Evaluation Framework

When an AI agent considers recommending your product, it evaluates several factors. Marcus identified the key ones by analyzing hundreds of AI-generated recommendations.

Factor 1: Clarity of Purpose

Can the AI agent explain what your product does in one sentence? If your homepage says you "leverage synergistic solutions to enable digital transformation," the AI struggles to categorize you.

Compare these two:

Unclear: "We're a next-generation platform that empowers teams to unlock insights."

Clear: "Segment is a customer data platform that collects, cleans, and routes customer data to analytics and marketing tools."

AI agents recommend products they can clearly describe. Vague positioning kills discoverability.

Factor 2: Use Case Specificity

AI agents match products to use cases. If your content doesn't explicitly address specific use cases, you won't get recommended for them.

Stripe's documentation doesn't just say "payment processing." It has specific guides for SaaS subscription billing, marketplace payments, platforms, e-commerce. When someone asks "What payment processor works for SaaS subscription billing?", Stripe gets recommended because that exact use case is documented.

Factor 3: Proof Points

AI agents weight quantified results heavily. Customer logos alone don't matter. Numbers do.

"Used by 5,000+ companies" is okay. "Reduces churn by 25% on average, used by companies like Slack and Figma" is better. Specific metrics + recognizable names = credibility.

Factor 4: Comparison Clarity

If you're a Salesforce alternative, say it explicitly. AI agents rely on clear competitive positioning to make recommendations.

Notion's documentation includes sections on "Notion vs. Confluence" and "Notion vs. Asana." When someone asks how Notion compares to these tools, the AI has authoritative information from Notion itself, not just third-party reviews.

Factor 5: Implementation Clarity

Can the AI agent explain how to get started? Products with clear onboarding paths, pricing transparency, and low-friction trials get recommended more often because the AI can confidently tell users what happens next.

The Three Pillars of AI Agent Optimization

Marcus built his strategy around three core pillars.

Pillar 1: Information Architecture

Structure your web presence so AI agents can extract key facts quickly. This means clear homepage messaging that states what you do in one sentence, dedicated use case pages for each major use case, comparison pages for key competitors, pricing page that's public and easy to parse, and documentation that's comprehensive and updated.

Think of your website as an API for AI agents, not just a marketing site for humans.

Pillar 2: Semantic Clarity

Use language that AI agents understand and users actually search for. This means industry-standard category terms, not made-up jargon, explicit use case language, clear capability descriptions, and quantified value propositions.

If your category is "customer data platform," use that term consistently. Don't call yourself a "data enablement orchestration layer" because you think it sounds more innovative.

Pillar 3: Authority Signals

Build external validation that AI agents can reference. This means customer case studies with metrics, third-party reviews on G2 and Capterra, industry analyst mentions, integration partnerships, and technical content that demonstrates expertise.

AI agents trust information that's corroborated across multiple sources. One source claiming you're great is marketing. Multiple independent sources is evidence.

Marcus's First-Month Implementation

Marcus started with quick wins that required no engineering.

Week 1: Audit and clarify. He reviewed their homepage, stripped the jargon, and rewrote their one-sentence description to be crystal clear. Old: "We unlock the power of data for modern teams." New: "We're a B2B analytics platform that tracks product usage, revenue metrics, and customer behavior for SaaS companies."

Week 2: Document use cases. He created dedicated landing pages for their three main use cases: product analytics for PLG companies, revenue analytics for sales-led B2B, and customer health scoring for CS teams. Each page had clear descriptions and customer examples.

Week 3: Add proof points. He updated their case studies to include specific metrics and added a "Results" section to their homepage showing aggregate customer outcomes.

Week 4: Monitor AI recommendations. He set up a system to test what ChatGPT and Claude recommended when asked about analytics platforms for different scenarios.

Results after 60 days: AI-attributed inbound increased from 12% to 23% of total pipeline. Demo requests from AI recommendations converted at 2.3x the rate of organic search.

The Uncomfortable Truth

Most companies optimized their web presence for Google in 2010 and haven't fundamentally rethought it since. They assume the same tactics work for AI agents.

They don't. AI agents don't care about keyword density or backlink profiles. They care about clarity, specificity, and authoritative information they can synthesize into recommendations.

The companies winning AI agent recommendations are doing three things: making their value proposition immediately clear, documenting specific use cases explicitly, and building verifiable proof points.

If you're not showing up in AI agent recommendations, you're missing the fastest-growing discovery channel in B2B software. Start with clarity. Make it obvious what you do, who you serve, and why you're credible. Everything else builds from there.