Nozomio

What do they actually do

Nozomio ships Nia, a hosted service that crawls and indexes code repositories and documentation, keeps that index up to date, and exposes it to AI coding assistants so they can retrieve relevant project context before calling a model. Developers connect Nia to their repos and docs, and assistants query Nia via a small server/protocol the team provides (an MCP server) to get the right files, snippets, or summaries on demand product site • MCP integration docs.

In practice, users sign up, point Nia at their GitHub or private repos and documentation sites, then add a provided integration so their assistant (e.g., Cursor, Continue) asks Nia for context first. Nozomio offers example agent rules/config and an API, plus a web dashboard to manage indexing jobs and keys context-sharing/docs • agent rules examples • docs.

The company says Nia is used by 1,000+ engineers and has raised a $6.2M seed round; it also reports internal benchmarks showing a measurable lift in agent accuracy when Nia indexes external docs (company-reported) homepage • fundraise post • YC profile.

Who are their target customer(s)

• Engineering teams at mid-sized SaaS companies: Engineers waste time chasing the right file or pasting links into assistants because assistants lack up-to-date knowledge of the team’s APIs, versions, and architecture across many repos and docs.
• Individual developers using AI coding assistants in their editor: They get wrong or irrelevant suggestions when the assistant doesn’t know project-specific details, forcing manual doc searches and corrections.
• New hires and rotating engineers ramping on a codebase: Onboarding drags because architecture, dependencies, and internal docs are scattered or stale, leading to repeated questions and slow ramp-up.
• Tooling, security, or platform teams at larger companies: They need to provide current private docs to internal assistants without copying sensitive content into third-party tools or maintaining fragile custom integrations.
• Product or support teams with API/docs-facing agents: Agents answer incorrectly when not grounded in the latest product docs, creating customer confusion and more manual ticket handling.

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

• First 10: Leverage founders’ and YC networks plus early signups; personally onboard each team with a short free pilot and hands-on setup to ensure end-to-end success and gather case notes.
• First 50: Enable self-serve (GitHub/IDE integration + free trial), publish concrete how‑tos and benchmark writeups, and run targeted community outreach with integration partners to convert active free users to paid.
• First 100: Stand up light SDR/AE coverage for account-based outreach to mid-sized SaaS orgs; run short paid pilots with clear success metrics and prioritize SOC2/enterprise features and dedicated onboarding to close platform/security teams and channel partners.

What is the rough total addressable market

Top-down context:

The market is developers and teams paying for better project context for AI coding assistants. Using global developer counts (28.7M) and per‑seat pricing anchors from Copilot and Sourcegraph, a top‑down range is roughly $6.5B–$16.9B; limiting to developers already using AI tools (~62%) gives about $4.1B–$10.5B Statista • Stack Overflow 2024 • Copilot pricing • Sourcegraph pricing.

Bottom-up calculation:

AI‑tool users ≈ 28.7M × 62% ≈ 17.8M; multiplied by $228–$588/year per developer gives ≈ $4.1B–$10.5B addressable today. Industry reports place “AI code tools” in the single‑digit billions, consistent with the low end of this range Stack Overflow 2024 • Copilot pricing • Sourcegraph pricing • Grand View Research.

Assumptions:

• Per‑developer pricing is in the Copilot/Sourcegraph range ($19–$49 per user per month).
• Share of developers actively using AI tools (~62%) is a reasonable proxy for addressability near‑term.
• Teams are willing to pay for a separate context layer at similar per‑seat price points as adjacent dev tools.

Who are some of their notable competitors

• Sourcegraph (Cody): Indexes repos continuously and powers its Cody assistant with that context; teams get an integrated index + assistant rather than a neutral context layer for multiple agents how Cody provides context.
• GitHub Copilot Chat / knowledge bases: Lets teams ground Copilot Chat in repo docs and knowledge bases (and supports MCP), giving repo‑aware suggestions inside GitHub’s ecosystem GitHub docs.
• Cursor: An AI‑first editor that indexes your workspace and external docs to return relevant snippets; attractive for individuals/small teams that want indexing and the assistant inside one tool Cursor docs.
• Pinecone (vector DB): Infrastructure teams can assemble their own retrieval layer with vector DBs and RAG toolkits instead of buying a hosted context service; offers flexibility but requires more build/ops RAG overview.
• deepset Haystack: Open‑source framework for building RAG pipelines and connectors to run indexing/retrieval on your own infra; a DIY alternative to a hosted, agent‑friendly context layer Haystack tutorials.