AthenaHQ

What do they actually do

AthenaHQ helps marketing teams understand and improve how their brand shows up in AI chatbots and generative search experiences (e.g., ChatGPT, Gemini, Perplexity, Claude). The product automatically queries multiple engines, captures where a brand is referenced and which sources are cited, and reports visibility metrics like impressions and citation rate homepage demo video.

In the app, users get a dashboard that highlights gaps versus competitors and specific fixes (e.g., which pages to publish or structure, and content briefs to ship). Athena also tracks LLM “traffic”/response volume and offers an enterprise citation management layer (Athena Citation Engine, or ACE) to help teams manage official sources that AI systems are more likely to cite case study pricing/ACE ACE announcement. The company sells a self‑serve subscription and enterprise plans with SSO and white‑glove setup pricing.

A typical workflow starts with a free audit or connecting a domain, reviewing the citation/impressions dashboard, applying prioritized recommendations (page structure, docs/publication, content briefs), then re‑measuring to confirm lift. A published case study reports a 50% increase in demos influenced by AI search after implementing Athena’s recommendations homepage how‑to case study.

Who are their target customer(s)

• Head of Marketing at an SMB SaaS company: Leads from AI chatbots are weak because answers don’t mention their product or cite competitors. They can’t measure the gap or run structured experiments to see what content AI systems prefer.
• E‑commerce/product manager: Products don’t appear in chatbot recommendations, hurting discovery and sales. Preparing canonical product data and exposing it in the right way for AI systems feels technically complex and hard to prioritize.
• SEO/performance marketing agency: Clients want visibility on ChatGPT-like tools, but the agency lacks audit tooling and repeatable playbooks to diagnose citation gaps or prove impact, making the work hard to sell and renew.
• Head of Digital/Brand at an enterprise: AI answers surface incorrect or unvetted brand info, creating compliance/reputation risk. They need centralized source control, enterprise features (SSO, permissions), and auditable outcomes before committing budget.
• Product/docs owner or technical writer: Help center/docs aren’t being used as sources by AI systems, leading to incomplete or incorrect answers. They need clear steps to structure/publish content so automated systems can find and cite it.

How would they acquire their first 10, 50, and 100 customers

• First 10: Founder‑led pilots: hand‑pick high‑fit SMB SaaS, ecommerce, and agencies, run the free AI‑visibility audit, deliver three prioritized fixes, and re‑measure to show lift. Convert pilots with a short case study and a simple monthly plan.
• First 50: Tighten the free audit → onboarding flow (templates, downloadable reports) and drive trials via targeted LinkedIn, short webinars, and email sequences. Partner with a few agencies to resell productized audits and publish resulting case studies.
• First 100: Add channel distribution (CMS/plugins, marketplace listings, docs/product integrations) and focused paid acquisition. Formalize an agency program with co‑sell assets/rev share, and run SDR outreach to enterprises with an ACE/SSO package.

What is the rough total addressable market

Top-down context:

GEO (generative engine optimization) sits adjacent to SEO software and search marketing budgets. Search remains a major spend category, and market researchers estimate SEO software to be a multi‑tens‑of‑billions category—indicating substantial budget pools that can shift toward tools that improve AI‑answer visibility Statista search ads Grand View Research.

Bottom-up calculation:

Conservatively, assume three buyer groups: 60k SMB/mid‑market SaaS and ecommerce brands adopt at an average $1.5k/year ($90M), 8k SEO/performance agencies at $3k/year ($24M), and 4k enterprises at $18k/year ($72M). That yields roughly $186M initial TAM, expanding as engine coverage and product‑feed integrations broaden use cases.

Assumptions:

• Only a small fraction of the total universe of brands/merchants are near‑term adopters (e.g., 60k of many millions).
• Average contract values: SMB $1.5k/year, agencies $3k/year, enterprise $18k/year, consistent with a self‑serve + enterprise SKU mix pricing signals.
• 3–5 year horizon; excludes adjacent services revenue and assumes no double‑counting between agency and brand licenses.

Who are some of their notable competitors

• Yext: Enterprise knowledge‑graph and listings platform positioning as an “AI‑ready” source of truth; overlaps where brands need structured, canonical data that AI systems can cite.
• Profound: AEO/GEO platform that queries major AI engines, reports visibility/citation metrics, and provides content/workflow tools—similar see → fix → re‑measure loop.
• Promptmonitor: Specialist GEO/AI‑visibility tool tracking brand mentions and source citations across ChatGPT, Gemini, Perplexity, etc., with monitoring and basic remediation suggestions.
• Otterly.ai: AI‑search monitoring and audit product checking where brands/pages are cited by AI search (ChatGPT, Google AI Overviews, Perplexity) with on‑page recommendations and tracking.
• LLM Pulse: Prompt tracking/monitoring across LLMs with response comparisons and source analysis; overlaps on routine monitoring and cross‑engine comparisons.