BitBoard launches agentic analytics workspace for humans and AI agents
BitBoard (YC P25) is an analytics workspace where humans and AI agents collaborate on shared dashboards, data primitives, and semantic models, with built-in provenance and verification infrastructure.
Score breakdown
BitBoard's shared provenance and verification layer directly addresses the core failure modes agents face in data analysis — bad inferences from missing business context and unverifiable outputs — by making agent work observable and sign-off-able by human teams.
- 01BitBoard is an agentic analytics workspace launching collaborative dashboards for humans and AI agents, backed by YC P25.
- 02Founded by Connor and Ambar, who previously built AI agents for healthcare administration.
- 03Legacy BI tools and AI chat tools are both identified as inadequate: BI can't give agents meaningful control; chat tools make analysis ephemeral.
BitBoard is an agentic analytics workspace that lets humans and AI agents collaborate on live dashboards and data analysis. The founders, Connor and Ambar, identified two core problems: legacy BI tools were never designed for AI users and can only bolt on chatbots without giving agents meaningful control, while AI chat tools treat analysis as ephemeral, making reporting and collaboration difficult. BitBoard's answer is a shared layer of data primitives — including canonical sources, entities, and measures via a semantic model — that both humans and agents can contribute to and read from.
The platform is built on DuckDB and Apache Arrow for columnar analysis, and uses a collaboration engine with isomorphic updates for humans and AI.
The platform is built on DuckDB and Apache Arrow for columnar analysis, and uses a collaboration engine with isomorphic updates for humans and AI. Every answer comes with provenance, and identical calls with identical parameters return identical numbers, enabling verification. Dashboards progressively incorporate intelligence, starting from SQL or code queries and scaling up to fully embedded apps. For longer-running agentic work, BitBoard provides agent containers and traces, giving agents a measurable goal and a way to verify their own work — turning agent outputs into datasets, dashboards, and traces that teams can observe and sign off on.
The company's original product was AI agents for administrative tasks in healthcare, but customer demand repeatedly pulled them toward data analysis challenges: queries scattered across disparate sources and spreadsheets with no shared context. BitBoard's design philosophy favors applying LLM judgment to discover problems, then generating deterministic software to automate solutions.
Key facts
- 01BitBoard is an agentic analytics workspace launching collaborative dashboards for humans and AI agents, backed by YC P25.
- 02Founded by Connor and Ambar, who previously built AI agents for healthcare administration.
- 03Legacy BI tools and AI chat tools are both identified as inadequate: BI can't give agents meaningful control; chat tools make analysis ephemeral.
- 04Shared data primitives include canonical sources, entities, and measures via a semantic model that both humans and agents can contribute to.
- 05Every answer includes provenance, and the same call with the same parameters always returns the same number.
- 06Built on DuckDB and Apache Arrow for columnar analysis, with agent containers and traces for long-running agentic tasks.
- 07Design philosophy: use LLM judgment to discover problems, then generate deterministic software to automate them.
Topics
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