BitBoard is an AI-agent-driven analytics workspace, positioned to make large language model (LLM) analysis results truly trusted by data teams.
Purpose
BitBoard is used to connect an enterprise's business data sources, quickly obtain analysis answers through natural language question-and-answer, and solidify these answers into shareable, reusable real-time dashboards. It is aimed at analysis scenarios that need to balance "speed" and "accuracy," emphasizing that answers must not only come quickly but also withstand verification.
Main Capabilities
- Natural language Q&A: Users ask questions in natural language, and BitBoard obtains answers from connected data sources, along with proof, that is, the basis for the answer's source.
- Generate real-time dashboards: For repetitive analysis needs, real-time dashboards can be generated, and the dashboards run based on trusted data sources and queries.
- Team context accumulation: Agents use the team's definitions, trusted queries, and historical correction information, so that each analysis reflects how the business actually operates, avoiding inconsistent standards.
- Fully traceable: Sources, queries, code, and context are all attached to the results, making them easy to view and review.
- Reuse trusted patterns: Trusted analysis patterns can be saved, and analyses can then be rerun directly, without having to start from scratch every time.
How to Use
The core process described officially is: after connecting data sources, ask questions in natural language to get answers with proof; for recurring analyses, turn them into real-time dashboards; agents combine team definitions and trusted queries to carry out subsequent analysis; all analysis processes and sources are traceable, and support saving and rerunning. A free trial is officially provided (Try BitBoard for free).
Applicable Scenarios
- Data teams need AI-assisted analysis results to be explainable and traceable.
- Business analysis needs to quickly answer ad hoc questions while ensuring that answers are accurate and trustworthy.
- Teams need to share analysis standards, reduce repetitive work, and solidify common analyses into reusable assets.
- Scenarios that need to keep AI analysis results consistent with the team's existing definitions, queries, and corrections.
Limitations
Regarding specific pricing plans (other than the free trial), supported data source types, deployment methods, platform compatibility, and whether an API is provided, the current information does not yet provide these.