Your competitors are becoming
the default recommendation.
Some companies consistently show up when buyers research a category. Others don't.
As buyers increasingly use ChatGPT, Claude, Gemini, and Perplexity to evaluate providers, platforms, and solutions, the companies that get recommended become the shortlist. Most founders have no visibility into whether they're being recommended, ignored, or replaced by competitors.
Industries We Commonly Analyze
Markets we cover.
Category dynamics, competitive positioning, and recommendation patterns vary significantly across markets. We focus on industries where evaluation cycles, buyer consideration, and category leadership matter.
Healthcare
- Behavioral Health
- Patient Engagement
- Care Management
- Clinical Workflows
- Digital Health
- Healthcare AI
Financial Services
- FinTech
- Compliance
- Banking Infrastructure
- Risk & Fraud
- Payments
- Financial Operations
Developer Tools
- Observability
- DevOps
- Security
- Cloud Infrastructure
- API Platforms
- Developer Productivity
B2B SaaS
- CRM
- SalesTech
- HRTech
- Customer Support
- Analytics
- Workflow Software
Sample Findings
What the report
actually shows.
A sample of how findings are structured across three formats: an annotated AI response, a category recommendation map, and an anonymized case narrative. Your report covers your specific company and category.
Annotated AI response
Query
What are the best AI-powered sales intelligence tools for early-stage B2B startups in 2024?
Response
For early-stage B2B startups, the most recommended AI sales intelligence tools are:
Sources: G2, TechCrunch, Founder testimonials (2024)D
Category ownership
Apollo owns this use case in AI results. First, consistent, across all four systems. Your report shows what is driving that and where you sit relative to it.
Qualifier language
Notice how Clay is qualified immediately: 'steep learning curve,' 'technical teams.' That shrinks the audience who would consider them. Your report shows what qualifiers AI attaches to you.
Feature reduction
Instantly is reduced to cold email. One feature, no depth. This happens when AI does not have enough signal to say more. Your report tells you if this is happening to you.
Source influence
G2, TechCrunch, 'Founder testimonials.' These are the sources the AI drew from. Your report maps which sources are shaping what is being said about you.
Category recommendation map
Illustrative. Your report maps 4 systems across 20+ queries in your specific category.
Case narrative (anonymized)
What they expected
A workflow automation company in the revenue operations space. Active content program, strong G2 reviews, recent funding announcement. They assumed they would appear alongside two established players in their category. They expected Claude and ChatGPT to reflect their enterprise positioning.
What the report found
Across 24 queries tested, the company appeared in two responses, both on Perplexity, both in passing. On ChatGPT and Gemini, three competitors were named consistently. Claude returned the company once, with a description that reflected their 2022 positioning, not their current one.
Key findings
Competitors surfaced
Three named consistently across ChatGPT and Gemini. One named on Perplexity and Claude only.
Primary gap
No analyst coverage. No third-party editorial links. The company appeared in G2 reviews but these were not cited by any system tested.
Positioning discrepancy
Internal language described the product as enterprise-grade. External coverage described it as a tool for small teams. AI systems reflected external coverage.
Near-term action
One Forbes Councils contributor article and two updated G2 review responses. Both were indexed within four weeks. Mention frequency on Perplexity increased.
Illustrative of report structure. Actual client findings are confidential. All names and data points are anonymized.
The Problem
Buyers are using AI to research solutions. You have no idea what they find.
When a buyer asks an AI system for recommendations in your category, a shortlist forms instantly. The companies in the first two or three responses become the consideration set. Most buyers do not look further.
The companies at the top are not always the best products. They are the ones with the clearest signal: the right third-party references, the right category language, the right comparison content in the right places.
This compounds. A company that appears consistently becomes associated with the category. That association shapes which companies get shortlisted, which get demos, and which get bought. Most founders do not know it is happening until pipeline is already affected.
No second page
When an AI names companies, buyers act on the first two or three. There is no scrolling, no refinement, no page two. If you are not in the opening response, you are not in the conversation.
Category default
Once an AI system maps a company to a category, it surfaces that company across a wide range of related queries. Category position compounds. Getting it wrong early is costly.
Consideration risk
The real risk is not low visibility. It is being excluded from evaluation before you ever know a buyer was in market. You cannot compete for a deal you were never part of.
How It Works
AI recommendations
are not random.
When an AI system recommends a competitor over you, it is rarely about product quality. It is about signal clarity. The outputs are probabilistic and vary across queries, but at volume, consistent patterns emerge. Those patterns are driven by five specific factors.
Category positioning
How clearly and consistently a company is described as a leader in a specific category, across multiple external sources. When described differently on your website, in reviews, and in press, the signal is ambiguous and AI systems respond accordingly.
Third-party references
Coverage in editorial publications, analyst reports, and review platforms. A company with two well-linked editorial mentions often surfaces more consistently than one with better documentation and a weaker external footprint.
Comparison content
Review content, competitor comparison pages, and alternatives articles that place a company against others. Companies that appear in these explicitly tend to surface more consistently in category queries.
Market signals
Case studies, customer names, and industry-specific language that indicates which market a company serves. A well-indexed case study with a recognizable customer name changes how a company is described, regardless of the actual customer mix.
Positioning consistency
Whether a company's own language and the language used about it externally say the same thing. When they diverge, AI systems default to what external sources say. That is often where the most fixable gaps are.
The Four Systems
ChatGPT
Returns varied results across queries, but consistent patterns emerge from the sources it most frequently cites: indexed web content, brand mentions, and user-generated reviews. Running many queries reveals which companies appear reliably versus occasionally.
Key signals: Web authority · Brand mentions · Category keywords
Gemini
Outputs shift across sessions and query phrasings, but a signal pattern is visible at volume: G2 and Capterra reviews, Google Search rankings, and structured business data appear to influence results more than on other systems.
Key signals: Search signals · Review platforms · Knowledge Graph
Claude
Tends to be more conservative about naming specific companies, but when it does, the results appear to reflect positioning clarity and editorial coverage quality rather than raw mention volume. Inconsistent across phrasings.
Key signals: Editorial coverage · Positioning clarity · Documentation
Perplexity
Cites sources directly, making it the most transparent of the four. Source authority and recency appear to matter more here. The citations it returns give direct insight into what is shaping the output for a given query.
Key signals: Citation quality · Recency · Source diversity
Because outputs vary across sessions and query phrasings, a single prompt tells you very little. The report runs 20 to 30 queries per system to surface what is consistent, which is where the actionable signal is.
The Methodology
Why this cannot
be replicated with
a few prompts.
You could open ChatGPT right now and ask what it recommends in your category. The problem is not running the prompts. It is knowing what the outputs mean, what is driving them, and what would change them.
Asking AI yourself
One query. One response. No baseline.
The report
20 to 30 queries, four systems. Consistent patterns separated from query-level variance.
Asking AI yourself
You see who appears. Not why.
The report
Competitor signals mapped: the specific sources, language, and references driving their recommendations.
Asking AI yourself
No competitive context. No comparison.
The report
Side-by-side analysis of what each competitor has indexed that you do not.
Asking AI yourself
No way to tell signal from noise.
The report
Pattern recognition across systems to identify what is consistent versus incidental.
Asking AI yourself
No path from output to action.
The report
Findings translated into specific, prioritized changes in positioning, PR, and category language.
Asking AI yourself
The report
One query. One response. No baseline.
20 to 30 queries, four systems. Consistent patterns separated from query-level variance.
You see who appears. Not why.
Competitor signals mapped: the specific sources, language, and references driving their recommendations.
No competitive context. No comparison.
Side-by-side analysis of what each competitor has indexed that you do not.
No way to tell signal from noise.
Pattern recognition across systems to identify what is consistent versus incidental.
No path from output to action.
Findings translated into specific, prioritized changes in positioning, PR, and category language.
Pattern recognition
A single query produces a single answer. Running 20 to 30 targeted queries across four systems reveals what is consistent. Consistency is the signal. What varies is noise. What does not vary is your actual competitive position.
Competitor analysis
Knowing a competitor appears first is a data point. Understanding why, what sources they have indexed, what category language they use, what third-party coverage they have built, is the analysis. That interpretation requires cross-referencing outputs with the external footprint that is driving them.
Source attribution
AI systems do not explain what they draw from. Identifying the four or five sources shaping how you are described requires cross-referencing outputs with citation patterns across systems. That is where the actionable gaps are.
Category interpretation
Knowing you appear less than a competitor does not tell you what to change. Translating an AI output pattern into a specific action, whether on your website, in your PR, or in your category positioning, is the work that requires judgment.
Strategic prioritization
There are typically ten to fifteen factors that contribute to competitive recommendation patterns. Most require real time and effort to address. The report identifies which three to prioritize, based on what is actually driving the gap, not what sounds plausible.
What You Get
Eight deliverables,
48 hours.
Everything is specific to your company and your category. The goal of each deliverable is not just to show you what is happening. It is to tell you what to do about it.
Category Placement Report
What category each AI system puts you in, and whether it is the right one. If they are filing you in an adjacent category, you will not surface on the queries your buyers are actually asking.
Competitive Positioning Analysis
Who appears instead of you, how consistently, and with what description. Most founders find two or three competitors here with weaker products but stronger positioning signals. That gap has a specific cause.
Query Coverage Map
20+ real queries across all four systems. Every appearance logged, every absence noted. The pattern of where you show up, and where you disappear, is usually where the problem lives.
Source & Citation Audit
The specific sources AI systems are using to form opinions about you. Usually four or five places. Often not what you'd guess. This is typically the most actionable finding.
Positioning Gap Analysis
The gap between what you say about yourself and what AI systems actually understand you to be. When these don't match, it tells you exactly what's not getting through.
Prioritized Action Plan
A ranked list of specific changes most likely to improve your competitive positioning, based on what is actually driving the gap. Not a checklist of fixes. Changes you can act on this week.
Loom Video Walkthrough
A full walkthrough of the findings and the action plan. So you can rewatch it, share it with your team, or come back to it in 60 days when you're running the follow-up.
30-Minute Review Call
A live call to work through the findings. Most useful for questions about the action plan and deciding what to tackle first.
Who Does This
Founder
AI Category Intelligence
[Founder bio goes here. Two sentences, first person, focused on positioning and AI systems experience.]
AI Category Intelligence is an independent competitive intelligence practice focused on one problem: why certain companies consistently dominate AI-driven recommendation sets in their category, and what distinguishes their positioning from companies that do not appear.
The methodology draws on sustained query research across ChatGPT, Gemini, Claude, and Perplexity, combined with competitive analysis of the external signals that AI systems demonstrably weight when forming category opinions: editorial coverage, review platforms, and comparison content.
Competitive positioning
Worked with B2B founders across SaaS, fintech, and professional services on category positioning in crowded markets.
AI systems research
Sustained query research across ChatGPT, Gemini, Claude, and Perplexity: what signals they weight, how category associations form, and what shifts them.
Market category analysis
Tracking the specific factors that cause some companies to consistently dominate category recommendations while others with comparable products remain absent.
The Process
From request
to report in 48 hours.
The analysis is thorough, but the timeline is fixed. Every report follows the same process, delivered within the same 48-hour window.
Request your report
Book a brief intake call via Calendly. Share your company URL, a short description of your market position, and your primary competitors.
Analysis begins
We run your company across four AI systems using 20+ targeted queries and a full competitor comparison. Every query is logged, analyzed, and cross-referenced.
Report compiled
Findings are compiled into all eight deliverables. The Loom walkthrough is recorded. Everything is checked against the raw query outputs before delivery.
Delivered and reviewed
You receive the complete report package. We then schedule your 30-minute review call to walk through findings and answer questions.
Pricing
$799
One-time. No retainer, no subscription, no ongoing fees. A follow-up report in three to six months is a separate engagement.
Category Perception Analysis across 4 AI systems
Competitive Discovery Analysis: who appears and how often
AI Recommendation Mapping across 20+ queries
Source and Citation Analysis
Positioning Gap Analysis
Strategic Action Plan
Loom video walkthrough
30-minute review call
Questions
Common
questions.
Right now, buyers in your space are asking AI systems to recommend solutions. Find out what they're hearing.
The report maps exactly where you appear, what competitors are being recommended over you, why, and what would change that. Delivered in 48 hours.
$799 one-time
Newsletter
How AI discoverability works
Occasional dispatches on GEO, AI category positioning, and the signals that shift how AI systems recommend companies. No noise.