Natural Language Data Queries

Hansei lets someone ask a question in plain language and get an answer drawn from the company's connected data, instead of writing a query by hand. A Beam agent can read a question that matches a pattern the team has already approved, such as a weekly sales total or a headcount by department, and confirm Hansei's answer against the source figures before passing it along. It notes the answer in the relevant report or chat thread. When a question touches a topic not pre-approved, such as financial forecasts or personnel details, the agent does not answer and instead sends it to a data analyst.

AI Data Assistants

Hansei's assistants are set up around a specific dataset, such as support tickets or product usage, so answers stay grounded in what that dataset actually contains. A Beam agent working with an assistant can read a routine question, apply the scope the assistant was configured for, and post the answer back to whoever asked, along with a note on which dataset it came from. It does not expand the assistant's scope on its own. If a question falls outside the configured dataset, or the answer looks inconsistent with a known figure, the agent flags it for a human to check first.

Query History and Audit Trail

Every question asked through Hansei and the answer it returned can be reviewed later, which matters when a number from a chat ends up in a report or a decision. A Beam agent can read that history on a schedule and note which questions were asked most often, or which answers were later corrected, into a shared log the team keeps for reference. It writes only to that log, not to the underlying data. Where a pattern suggests an assistant is giving inconsistent answers to the same question, the agent surfaces the discrepancy to a human rather than trying to resolve it.