Connect your data. We surface where your agents will fail and generate the policies to stop it.
Missing context gets found, written into policy, served to every agent, and recorded once it is used.
Connected assets get scanned for meaning that is absent or contested. Definitions that resolve more than one way, procedures that decide whether a figure is final, sources that disagree with no rule for which one applies.
Explore DiscoveryMost agent failures are predictable. Business rules scattered across wikis, columns with no authoritative definition, decisions with no audit trail. We find them before your agents do.
A decision-context layer for data agents. Surface the data and context behind a question, capture the judgment the agent should apply, and keep a log of the requests agents make.
Business logic, data contracts, policies, source provenance, access rules, and asset metadata as structured, machine-readable context.
Agents evaluate dynamic business rules instead of relying on hardcoded branches or human interpretation.
Context layers are versioned. A policy that changes doesn't break every system relying on it.
Data agents fill gaps with guesses. Those guesses compound across every run.
A column named revenue means different things in different tables. An agent sees a number. Without context, it can't tell the difference.
revenue_amount
3 conflicting definitions
Business logic is scattered across threads, wikis, and documents. None of it is readable by an agent.
retention_policy
No authoritative source
The moment data moves across a join, the definition of a field can change. The agent has no way to know.
customer_id
Definition changes across join
When an agent produces a wrong result, there is nothing to trace back to. No rule it followed, no decision it logged.
output_methodology
No decision log found