Company profile · 4 min read
BitBoard: persistent analytics for people and agents
BitBoard keeps data, queries, charts and agent context together in repeatable dashboards that teams can inspect and rerun.
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
Published · Updated
What it does
BitBoard is an analytics workspace where people and AI agents build persistent dashboards and reports together. A connected agent can load data, write SQL or code, create charts and commentary, and leave the logic and outputs attached to a shareable workbook. The YC profile explains the problem well: AI analysis is useful until a team needs a recurring report, shared context and a result that can be rerun.
The fit is a data or operations team that wants natural-language analysis without giving up the underlying query, source and correction trail. BitBoard is more promising for teams that need the answer again next week than for one-off chat exploration. Its homepage emphasizes three product qualities—accurate answers grounded in connected data, traceability of sources and queries, and repeatability of saved analysis.
Why I’d look closer
The product has a useful separation between external agents and BitBoard’s own Ask agent. The docs say teams can connect Claude, Claude Code, Cursor, ChatGPT, Codex or another MCP-compatible agent to build dashboards, while Ask is the built-in agent for grounded questions. That distinction makes the system easier to evaluate: one can inspect what an external agent authored and separately test what Ask can answer.
Pricing is public. The pricing page lists Individual as free with daily Ask limits and no credit card, Teams at $49 per seat/month with a shared workspace and centralized billing, and Enterprise as custom with admin, support and advanced-security features. The page says plans are flat-rate and do not currently bill for usage. The docs mark Ask as beta, so its capabilities and workflows may change.
The founder context matches the product’s ambition. The YC biographies describe Connor Jones with Forward, BlackRock and Columbia Engineering experience. They describe Ambar Choudhury as Forward’s first engineer and a former Palantir engineer who worked on government-facing technology. Those backgrounds support the emphasis on data controls and durable analysis, but they are not a substitute for testing a team’s actual warehouse and permissions.
What I’d ask
Which connectors are supported, how are freshness and row-level permissions enforced, and what exactly is executed when an agent writes code? I’d run a recurring report with a known query, deliberately introduce a correction, and check whether the saved workbook changes predictably. Enterprise buyers should inspect the security and administrative controls behind the pricing-page language rather than infer a certification.
My editorial take
BitBoard is a strong fit for teams that have outgrown ephemeral AI analysis but do not want a rigid BI chatbot. The core test is simple: can a teammate open the shared URL, understand the source and logic, and rerun the result without the original prompt author in the room?
Quick facts
| Field | Sourced detail |
|---|---|
| Buyer fit | Data, operations and analytics teams needing repeatable reporting |
| Public pricing | Individual free; Teams $49/seat/month; Enterprise custom |
| Agent surface | MCP-compatible external agents plus built-in Ask, marked beta |
| Differentiator | Sources, queries, code and context stay attached to the result |
Sources checked
Checked 2026-09-19.
| Source | Used for |
|---|---|
| YC company profile | Product definition and founder backgrounds |
| BitBoard homepage | Current product qualities and workflow |
| Pricing | Plans, flat-rate statement and Enterprise feature claims |
| BitBoard docs | Agent connections and Ask beta boundary |
Cohort context
BitBoard is listed in Spring 2025. In our 2026-09-18 directory snapshot, 97 of 143 listed companies in that cohort have YC’s primary industry label B2B (67.8%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.
Public website snapshot
Observed 2026-09-19T16:15:30.863Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.
| Signal | Homepage observation |
|---|---|
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Not observed in this response |
| Docs/developer link | Observed |
| Pricing link | Observed |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |
Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.
About the author
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.
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