Lapis

What do they actually do

Lapis tracks how AI models and AI search products (e.g., ChatGPT‑style assistants and AI browsers) use and cite a company’s website, then recommends concrete fixes to improve visibility in those answers. It crawls and “compresses” site content, fans out realistic questions across multiple models, measures citation frequency/placement/sentiment, and surfaces prioritized changes and simple implementations, with alerts and reports to tools like Slack (product, pricing).

The team positions the product as moving from measurement to action, with higher‑tier features like “AI Actions” and an “SEO Agent” that aim to automate fixes and ongoing optimization for AI search and AI‑native browsers (pricing, product).

Who are their target customer(s)

• SEO/content manager at a mid‑size SaaS or publishing site: They can’t see which AI models surface or cite their pages, so it’s unclear what content to change to gain AI‑answer visibility; current SEO tools don’t measure AI citations or conversational answers (product, pricing).
• Head of growth / B2B marketing lead: They need repeatable metrics to show executives if AI search is driving discoverability and leads, plus workflows to close gaps when models favor competitors (product, pricing).
• SEO agency or consultant managing many clients: Running hundreds of prompts across many models and repeating tests is manual and slow, making it hard to deliver consistent reports and fast, prioritized fixes at scale (product, blog).
• E‑commerce marketing/product owner: Product and help pages often aren’t cited in AI answers, hurting discovery and conversions; they want actionable changes and simple integrations (GA/Semrush/alerts) to fix that quickly (product, pricing).
• Product or content ops at an AI‑forward startup: They need to format and index content so AI browsers/agents will use and cite it, but lack tools to compress content into model context windows and validate changes across multiple engines (product, pricing).

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

• First 10: Use founder‑led outreach through YC and existing networks to secure time‑boxed pilots with mid‑size SaaS/publishing SEO or growth leads; handle setup and weekly reporting, then close on one clear win and capture a public case study (product, pricing, YC).
• First 50: Add a growth rep to run targeted outbound to SEO agencies and B2B marketing heads, run recurring demos/webinars that walk through crawl → query fanout → fixes, and convert attendees with short paid pilots and templated onboarding (product, blog, pricing).
• First 100: Launch a limited self‑serve tier/trial and publish vertical playbooks and case studies; establish two partner channels (one SEO tool, one agency) for co‑selling, and close larger accounts with an automated scan, prioritized action plan, and optional paid managed setup (pricing, product).

What is the rough total addressable market

Top-down context:

The closest comparable market is SEO/SEO‑software, estimated around USD ~74–75B in 2024; adjacent AI search engines and conversational AI add further spend that can flow into AI‑search visibility tooling (Grand View Research, Grand View Research, Fortune Business Insights).

Bottom-up calculation:

Illustrative: focus on mid‑market/enterprise buyers and agencies likely to pay for AI search visibility—e.g., 100,000 target accounts worldwide × $10,000 median ARR implies a $1.0B bottom‑up TAM for early AEO tooling; higher enterprise ARRs or broader adoption would scale this further. This complements, but does not replace, the larger top‑down SEO TAM.

Assumptions:

• Targetable accounts include mid‑market/enterprise websites with active SEO budgets plus multi‑client agencies; estimated at ~100k globally.
• Median contract value for AI‑search analytics/automation in early market ranges from $6k–$20k ARR; $10k used for simplicity.
• Adoption starts with early movers; bottom‑up TAM here reflects current practical buyers rather than the full SEO/martech spend.

Who are some of their notable competitors

• BrightEdge: Large enterprise SEO platform with AI‑assisted features and growing coverage of generative/AI search results—an incumbent with strong budgets and relationships.
• Semrush: Broad SEO/SEM suite used by mid‑market and agencies; increasingly adds features to track AI‑driven search surfaces, making it a likely substitute for some buyers.
• seoClarity: Enterprise SEO platform known for analytics and tracking of Google’s AI‑generated results; competes for the same measurement and reporting budgets.
• Botify: Enterprise crawling and SEO analytics focused on technical SEO and large sites; well positioned to extend into AI‑search readiness and reporting.
• Conductor: Enterprise SEO and content intelligence platform with deep reporting; could bundle AI‑search insights for customers looking to keep a single vendor.