AI GEO & AI SEO. Your category moves faster than the models describing it.
AI companies face a problem unique to their sector: assistants recommend competitors based on training data that is months out of date, describe products you have since rebuilt, and miss launches entirely. Staleness is the enemy.
A category where the answer is usually out of date
AI tooling turns over faster than any model's training cycle. The result is assistants confidently recommending yesterday's leader - which is either your biggest problem or your biggest opportunity.
What's the best AI tool for [task] right now?A recency prompt where models frequently answer from stale knowledge unless retrieval sources say otherwise.
Best open-source alternative to [product]High-intent displacement, decided by documentation, benchmarks and repository signals.
How does [your product] compare to [competitor] on accuracy?A benchmark prompt where published, methodologically sound evidence is the deciding factor.
Is [product] still the best option, or has something replaced it?The staleness question itself - and the clearest opening for a newer entrant.
We build your prompt set from your own category and run it against live models - these are illustrative.
Fighting the training cutoff
Every model has a knowledge cutoff and your category outruns it. Winning here means being present in the retrieval sources models reach for when they need current information, not just in the data they were trained on.
Retrieval beats training data
When a model searches rather than recalls, current, well-structured sources decide the answer. That is where the leverage is.
Evidence must be reproducible
Published methodology and reproducible benchmarks get cited. Marketing performance claims get discounted.
Repos, docs and papers count
Technical sources carry weight in this category that no landing page will ever match.
What AI-sector GEO involves
Recency & accuracy audit
We test what models currently say about your product and category, and document exactly where their knowledge is stale or wrong.
Retrieval source strategy
Ensuring current, structured, authoritative sources exist wherever a model looks when it needs up-to-date category information.
Benchmark & methodology publishing
Reproducible evaluation evidence published in the form models and technical readers both trust.
Docs, repos & technical presence
Documentation, repository and technical-community signals that establish current capability.
Reproducibility over claims
This is the most technically literate audience in any category, and models mirror that scepticism. Unsupported performance claims fail with both.
Benchmarks without published methodology are treated as marketing. With it, they become citable evidence.
Stating what your model or tool does not do well builds the credibility that makes the rest citable.
Version numbers, release dates and evaluation dates let a model reason about currency instead of guessing.
How we measure AI-sector GEO
- Recency and accuracy of model-stated product capability
- Citation share on category and alternatives prompts
- Benchmark and methodology citation frequency
- Displacement of stale competitor recommendations
- Documentation and repository citation signals
- AI referral traffic, trials and signups in GA4
- Before/after prompt testing on live models
- Time from launch to reflection in model answers
We work across regulated and high-consideration categories
AI-sector GEO questions
What is GEO for AI companies?
Why do AI assistants describe our product incorrectly?
Can you influence what a model says when it isn't searching the web?
How do we handle competitors being recommended from outdated data?
Do benchmarks actually help?
Isn't it strange to optimise AI products for AI discovery?
See what AI currently says about your product
We'll document where model knowledge of your product and category is stale or wrong, and show you what it takes to correct it.