nao Labs

What do they actually do

nao Labs ships a downloadable, desktop AI-first code editor (a fork of VS Code) built for data teams. It connects locally from your machine to your data warehouse (e.g., BigQuery, Snowflake, Postgres, Databricks, Redshift, DuckDB, Athena) so you can write SQL/Python/dbt and run queries directly against live data, with results previewed in the editor rather than as blind LLM outputs site/docs features HN launch.

The editor includes an AI tab/agent that understands your schema and codebase for schema-aware autocomplete and generation, plus dbt integration (model previews and lineage), Git/terminal, and side-by-side code and data diffs. It also supports quick data-quality checks and impact analysis to validate changes before committing features product demo HN. The app is live today (initially macOS, with Windows/Linux planned) and offers a free tier and a Pro plan; founders have mentioned ~$30/month for Pro downloads HN pricing founder post.

Who are their target customer(s)

• Data analyst: Writes ad‑hoc SQL and has to switch between an editor and the warehouse UI to check results; worries about referencing the wrong columns or breaking dashboards. nao provides schema‑aware autocomplete and live previews inside the editor to reduce that friction features product demo.
• Analytics engineer (dbt maintainer): Needs to author dbt models, preview changes, understand lineage impact, and add tests without risking production metrics. nao offers dbt model previews, lineage views, and test generation in the IDE to validate changes pre‑commit features blog.
• Data scientist: Combines SQL and Python against real data but LLM tools often hallucinate or suggest code that doesn’t match the schema. nao runs queries against the warehouse and uses schema context for more truthful suggestions and previews docs HN.
• Data engineer / pipeline owner: Spends time debugging ETL issues, tracking table usage, and running heavy SQL across engines. nao connects to major warehouses and surfaces code + data diffs to spot regressions faster site blog.
• Head of analytics / small‑team lead: Faces tool sprawl, code reviews, and data‑quality risk while needing privacy and governance. nao centralizes work with local connections and emerging governance/enterprise options to keep changes auditable and safe pricing/FAQ site/security & roadmap.

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

• First 10: Founder‑led onboarding of early users from YC, personal networks, and HN/Slack responders; run 1:1 sessions on the macOS app and ship fast fixes to lock in usage HN docs/download.
• First 50: Target dbt, analytics, and data‑engineering communities with demos and how‑to posts; drive trials via templates and iterate onboarding to shorten time‑to‑value product demo features.
• First 100: Layer product‑led expansion with light sales to small teams: partner listings, consultancy outreach, and paid pilots (e.g., ~$30/month Pro) while tracking usage to prioritize enterprise features pricing integrations/features.

What is the rough total addressable market

Top-down context:

The cloud data‑warehouse market is already in the tens of billions and projected to grow substantially (e.g., ~$28B in 2024 to ~>$100B by 2032), framing a large adjacent software opportunity for analytics/dev‑productivity tools Credence Research.

Bottom-up calculation:

Using dbt’s ~80,000 weekly teams as a high‑intent proxy and a ~$30/month ($360/year) seat price, 1 paid seat per team implies ~$28.8M ARR; 3 seats/team ~$86.4M; 10 seats/team ~$288M dbt pricing founder post.

Assumptions:

• dbt’s 80k weekly teams reasonably proxy early adopters for a schema‑aware IDE.
• Average paid seats per team fall in the 1–10 range for SMBs and small data teams.
• Price point remains around $30/user/month for the self‑serve/Pro tier.

Who are some of their notable competitors

• Hex: A web‑first analytics workspace for SQL/Python, notebooks, and data apps with dbt metadata; overlaps on “one place to explore + ship,” but is web‑native/notebook‑centric vs. a local desktop IDE dbt integration.
• Datafold: Specializes in data diffs, regression testing, and column‑level lineage for dbt projects; overlaps on preview/impact analysis but centers on CI/PR testing rather than an IDE with an agent for authoring code dbt/data‑diff.
• dbt Cloud (dbt Labs): Managed dbt developer experience with model previews, lineage, and growing QA/chat features; competes on dbt‑first workflows though not an AI‑first desktop editor features.
• PopSQL: Collaborative SQL editor with schema browser, autocomplete, and dbt support; targets analysts/small teams seeking a shared SQL tool, with less focus on agentic AI or local execution dbt integration.
• Mode: Cloud analytics platform with SQL editor, Python/R notebooks, and visual reporting; reduces tool‑switching for analysts but is a hosted BI/notebook product rather than a local, schema‑aware IDE with an agent SQL editor notebooks.