AI Data Authoring
Describe the outcome in plain language. Get back a working, validated data workflow.
A multi-agent pipeline turns a business request into a real workflow grounded in your actual connected data. A planner drafts the approach, specialist agents write each step, and a critic validates the result and repairs it until it compiles cleanly. You approve the plan before anything is generated.
- 01Read opportunities & accounts from Dynamics 365
- 02Exclude test accounts (business rule)
- 03Aggregate revenue by region and month
- 04Render trend chart
Most AI demos never touch your real data
Generic assistants produce plausible code against imaginary schemas. Business teams need output that's grounded in their own systems, respects their rules, and actually runs.
- 01
Ground
Reads the real schemas behind your connections and infers relationships, which it asks you to confirm rather than assume.
- 02
Plan
A planning agent drafts the workflow. You refine it and approve it. Business rules written in plain language become enforced constraints.
- 03
Generate
Specialist agents write each step in SQL, Python, JavaScript or a chart specification.
- 04
Validate & repair
A critic runs real compiler diagnostics and rule checks, then sends only the failing step back to be repaired.
What you get
Grounded in your schemas
Uses live schema introspection from Dynamics 365, SharePoint, S3 and other connected sources instead of guesswork.
Human approval built in
Nothing is generated until you approve the plan, and ambiguous relationships are shown to you for confirmation.
Business rules as constraints
Rules written in plain language are compiled into structured checks that every generated workflow must pass.
Provider-neutral
Works with Anthropic, Google Gemini or AWS Bedrock. Switching is a configuration change, not a code change.
Sovereign-ready
Runs fully on-premise with local open models, with no data leaving your network.
Plugs into your tools
Distributed as an MCP server, so it works inside AI assistants and IDEs or as a containerised service.