Company profile · 4 min read
AthenaHQ: Measuring and acting on AI-search visibility
AthenaHQ tracks how brands appear across AI search engines, analyzes citations and competitors, and recommends content and visibility actions.
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
Published · Updated
AthenaHQ is a measurement and action platform for brands trying to appear in AI-generated search answers. It fits a marketing team that already has content, a defined category and a reason to track whether ChatGPT, Perplexity, Gemini and other engines cite or recommend it.
What it does
AthenaHQ’s current homepage describes a command center for AI visibility tracking across 11-plus models, citation-source analysis, competitor monitoring, content recommendations and on-page and off-page actions. The product has separate surfaces for a GEO/AEO manager, an executive dashboard, content strategy and PR monitoring. The YC profile frames the same workflow as measuring what AI engines cite and recommend, then helping the team improve its presence.
The useful decision is not whether a brand can buy “AI SEO.” It is whether the team has a repeatable set of category questions, sources it wants to earn citations from and an owner who can act on the findings. A visibility dashboard without a content or product response loop is just a new rank report.
Pricing is public. The plans page shows an Essential option with $25 in free credit, Starter at $295 per month with 3,600 credits and paid API/additional-credit add-ons, and Enterprise custom. The page also repeats a $300/month free-credit label near Starter, so a buyer should confirm the actual subscription and credit economics. Enterprise adds options such as SSO, audit logs, multi-region support and executive BI integrations.
AthenaHQ publishes customer case-study claims including share-of-voice lifts, AI-driven lead growth and higher citation coverage. Those are vendor-selected outcomes, not independent measurements. The founder context is relevant: the YC record describes Andrew Yan’s product work on Google Search and DeepMind’s generative-media team, and Alan Yao’s early AI and consumer-product experience.
What could make it the wrong choice
AI answers change with query wording, location, source selection and model updates. The buyer should define a stable prompt set, separate mention from qualified recommendation and verify whether a cited source actually sends useful demand. The platform can show a visibility problem; it cannot make weak positioning or an untrusted product compelling by itself.
Editorial take
I would shortlist AthenaHQ for a company that already invests in content and wants a concrete AI-discovery operating loop. I would not buy it as a substitute for product-market fit or ordinary search fundamentals. Start with one category, one competitor set and one decision the marketing team can change this month.
Quick facts
| Field | Sourced detail |
|---|---|
| Product | AI-search visibility tracking, citation analysis and content actions |
| Buyers | Marketing, SEO, content, PR and enterprise growth teams |
| Public pricing | Essential free credit; Starter $295/month with 3,600 credits; Enterprise custom |
| Public outcomes | Case-study visibility and lead claims, company-reported |
| Main gate | Stable query set, source quality, action ownership and credit economics |
Sources checked
| Source | Checked |
|---|---|
| YC profile | 2026-09-19 |
| AthenaHQ homepage | 2026-09-19 |
| AthenaHQ pricing | 2026-09-19 |
| AthenaHQ launch | 2026-09-19 |
Cohort context
AthenaHQ is listed in Winter 2025. In our 2026-09-18 directory snapshot, 104 of 165 listed companies in that cohort have YC’s primary industry label B2B (63.0%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.
Public website snapshot
Observed 2026-09-19T16:19:24.006Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.
| Signal | Homepage observation |
|---|---|
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Not observed in this response |
| Docs/developer link | Not observed in this response |
| Pricing link | Observed |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |
Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.
About the author
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.
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