AI Answer Audit is a measurement service for B2B software companies. I measure what ChatGPT, Claude, Perplexity and Google AI actually say when someone asks a buying question in your category — which competitors get named, in what order, and which sources those answers are built from. Report in 72 hours.
Query: "what tool should I use to monitor LLM costs in production"
The answer names eight tools. Helicone is not one of them — and helicone.ai is cited as one of the sources the answer was built from. Their content was good enough to ground the answer. Their product was not in it.
One finding from the sample audit. Read the whole report →
| Where that company appears, by buyer intent | Named |
|---|---|
| Category discovery | 0% |
| Problem-first | 0% |
| Buying criteria | 0% |
| Comparison | 100% |
| Branded qualification | 100% |
Found only once the buyer already knows the name. Absent everywhere new demand is formed. This pattern is common, and most companies have never checked for it.
Across a query set built from your buyers' real decision path, not keyword volume. Share of voice is rank-weighted, because being named first is not the same as being named fifth.
Presence split across five buyer intents. This is usually the finding that changes what a team does next.
Every brand named in your category's answers, ranked by the share of the answer space they hold.
Which domains those answers are actually built from. It is usually competitors' own blogs — whoever writes those pages is writing your category's answer.
Crawler access, render dependency, structured data, comparison coverage, entity clarity and more — each scored, each with the specific change to make.
Handed over as JSON so you can re-run everything I did. The measurement is auditable on purpose.
If the report doesn't show you at least one specific, fixable reason you're being left out of answers in your category, you don't pay. If you've already paid, I refund it.
Honest limits. Assistant answers are non-deterministic and personalised. These are measurements at a point in time, not guarantees, and the report says so in its own method section. Site signals are measured from the homepage and site root. The only fair way to judge movement is re-running the identical query set — which is why the query set ships with every report.
Sonu Kumar. I build production RAG and agent systems, and I run AI Anytime, where I teach engineers to build them. The scanner behind this audit is open on GitHub. I started measuring this from the retrieval side rather than the marketing side, because retrieval is the part I actually work on — what gets chunked, what gets ranked, what makes it into a model's context, and what quietly never does. That is the same mechanism deciding whether an assistant names your product.
| Option | What it gives you | Cost |
|---|---|---|
| Asking ChatGPT yourself | One answer, one session. Non-deterministic and personalised, so it is a data point, not a baseline. | Free |
| AI visibility monitoring tools | A recurring dashboard of mentions. Good for tracking once you know what to fix; they do not tell you why you are absent or what to change. | $39–$500/mo |
| A GEO agency retainer | Strategy plus execution over months. Thorough, and the right answer at scale. | $5,000–$15,000/mo |
| This audit | A one-off measured baseline across five buyer intents, the reasons behind it, and a prioritised fix list you can execute yourself. | $297 once |
If you already know what to fix and just want it tracked, buy a monitoring tool instead — I will tell you that rather than sell you an audit. Longer comparison →
It is not a ranking measurement. Companies routinely rank on Google page one and are still never named by an assistant — the sample report shows exactly that, with a company cited as a source for an answer that names eight competitors and never names them.
You can, and you should — which is why the query set ships with the report. One ad-hoc question tells you little, because answers are non-deterministic and personalised. Dozens of observations across five intents with rank-weighted scoring tells you something.
Not exactly, and the report says so in its own method section. That is precisely why the deliverable is a baseline on a fixed query set.
Then the report says so plainly and you do not pay.
Send me your site and your top three competitors. I'll run the first few queries and send you what comes back — free, no call, and you keep it whether or not you buy anything.