What ChatGPT, Claude, Perplexity and Google AI tell your buyers when they ask about LLM observability — and why.
Helicone is found only when the buyer already knows the name. Across 3 of the 5 intents measured - the ones that create demand - Helicone was named in zero answers.
Assistants behave very differently depending on how the buyer asks. This is presence rate broken out by intent, ordered from coldest buyer to warmest.
| Category discoveryBuyer does not know vendors yet. Pure demand creation. | 0% | |
|---|---|---|
| Problem-firstBuyer describes a symptom, not a category. Earliest touch. | 0% | |
| Buying criteriaBuyer filters on features and price. Immediately pre-purchase. | 0% | |
| ComparisonBuyer already has a shortlist and is checking alternatives. | 100% | |
| QualificationBuyer knows your name and is validating it. | 100% |
Share of voice is rank-weighted — being named first counts for more than being named fifth, because that is how buyers read a list.
| 1 | Openobserve | 19.3% | 4 | |
| 2 | Langfuse | 14.6% | 5 | |
| 3 | Helicone you | 6.5% | 2 | |
| 4 | Phoenix | 6.3% | 3 | |
| 5 | Confident Ai | 4.8% | 2 | |
| 6 | Bifrost | 4.8% | 1 | |
| 7 | Comet | 4.1% | 2 | |
| 8 | Langsmith | 3.9% | 2 | |
| 9 | Truefoundry | 3.7% | 2 | |
| 10 | Datadog | 3.5% | 2 |
These are the domains the answer surfaces drew on. Whoever writes these pages writes your category's answer.
| openobserve.ai | 5 | competitor-authored |
| truefoundry.com | 4 | competitor-authored |
| confident-ai.com | 4 | third party |
| firecrawl.dev | 2 | third party |
| mlflow.org | 2 | third party |
| braintrust.dev | 2 | third party |
| getmaxim.ai | 2 | third party |
| dev.to | 2 | third party |
| signoz.io | 1 | third party |
| helicone.ai | 1 | your own domain |
Twelve signals decide whether a model can retrieve, parse and cite you at all. These are the ones costing you the most, worst first.
Only 125 words in raw HTML. The page is almost certainly client-rendered, so crawlers see a blank shell.
FixServer-render or pre-render your marketing and docs pages. Most AI crawlers do not execute JavaScript, so a client-rendered page looks empty to them no matter how good the copy is.
No useful JSON-LD found.
FixAdd JSON-LD: Organization + WebSite sitewide, SoftwareApplication or Product on the product page, FAQPage on anything Q&A-shaped. This is how a model resolves what you are and what you cost.
None of 1 headings are phrased as buyer questions.
FixRewrite headings as the questions buyers actually type. Models extract answers by matching a query to a heading and lifting the text beneath it.
No FAQ, lists or comparison tables - nothing shaped for extraction.
FixPut a 40-60 word direct answer immediately under each question heading, before any marketing copy. That paragraph is what gets quoted.
Only 1 comparison signal(s).
FixShip "<you> vs <competitor>" and "best <category> tools" pages. Comparison queries are where buyers use assistants most, and they are the pages models quote when ranking vendors.
Entity definition is partly present.
FixState plainly, in one sentence near the top: what the product is, who it is for, and what category it belongs to. Models need an unambiguous entity definition before they will recommend you.
Partial pricing signal.
FixPut real numbers on a public /pricing page in plain text. "Contact sales" means a model cannot place you in any budget comparison and will name a competitor that publishes numbers.
Only github.com.
FixGet listed and reviewed on the sources models actually cite for your category: G2, Capterra, Product Hunt, relevant subreddits, awesome-lists and roundup posts. Models trust third parties far more than your own site.
Recent year (2026) but no date metadata.
FixPublish visible dates and keep a changelog. Assistants systematically prefer sources they can date, and drop ones they cannot.
Honest limits. Assistant answers are non-deterministic and personalised; these are measurements at a point in time, not guarantees. Site signals are measured from the homepage and site root. A re-measurement on the same query set is the only fair way to judge movement — which is why the fix pack ships with the query set.