What it sells
Open Source Analytics Agent
Business model breakdown
Buyer: who owns the budget?
UnverifiedHead of data / analytics engineering at data-mature companies.
Trigger: what makes them buy now?
UnverifiedBusiness users want self-serve answers from the warehouse but LLM agents are unreliable without engineered context.
Workflow: what inputs become what output?
Data team builds agent context as a file system (nao init), syncs from warehouse, dbt/Looker repos and Notion, tests with unit tests on question-to-SQL (nao test), then deploys a chat UI or Slack/Teams bot (nao chat).
Economics: what it saves and how it charges
Open-source core; revenue from hosted cloud, enterprise plans and paid services (implementation, advisory, training, fixed-scope consulting days).
Distribution: how it reaches customers
Open source GitHub adoption and docs, then sales-assisted cloud/enterprise and services via discovery call.
Funding history and coverage
| Round date | Stage | Amount | Investors | Source |
|---|---|---|---|---|
| — | Y Combinator Spring 2025 | — | Y Combinator | ycombinator.com |
Profiles summarize public reporting and company websites. Business-model notes are MaxBC's reading of that material, not confirmed facts; unknowns are left blank rather than guessed. Updated 2026-10-12.