Your platform can already talk to your data. We make the answers trustworthy - governed, secured, and verified on the stack you already run.
Databricks Genie Ontology, Snowflake Cortex Sense, and Microsoft Fabric IQ generate context automatically. Genuine progress - but three failure modes keep it out of production.
The system ranks whatever definition exists as authoritative - not the one the business has certified as correct.
Out of the box, an auto-context assistant can surface data a given persona should never be allowed to see.
Fluent SQL can still be wrong. Correctness has to be curated and tested, not assumed.
The platform constructs the pillars natively. KPI decides what they contain, fills the gaps, and stays accountable for the result.
Concepts and rules, once, as a governed ontology and semantic model.
Live entities and relationships, so AI can reason across your data.
Technical schema mapped to certified business language.
The right context injected into the prompt at runtime.
Star schemas, descriptive names, comments, relationships - often the single biggest accuracy lever.
Expose only the tables each persona needs - gold-layer only, staging and raw hidden.
Persona / row-level access, PII masking, and role design - so each user sees only what they should.
Curated instructions and verified queries so the agent answers correctly, not just plausibly.
Golden questions, expected-SQL regression, and groundedness scoring gate every release.
Human-in-the-loop before actions, persona enablement, and cost-per-query under an SLA-backed run.
Your systems connected at the base, governed context at the centre, agents and people on top - all delivered on your platform.
The same four moves on every platform - only the native tooling changes.
Gold-layer modeling, schema filtering, and scoping per persona.
Certify glossary, metrics, and relationships with ownership and lineage.
Refine instructions and verified answers; secure every space.
Regression gates, then observe quality, cost, and drift - HITL before actions.
Same offering, same engagement, same SI value. Only the native capability names change.
We don't chase a model-accuracy score. A near-miss isn't reassuring when the answer that's off is the one someone acts on. So we work at the context layer: certified definitions, permission-scoped data, verified answers, and results parity-checked against the source. The context every answer stands on is fully governed and verified - which is why people trust the number enough to act on it.
A product sells you software to build context. We deliver governed, verified context - platform-native, on the stack you already run, with accountability you can put in front of a regulator.
A fixed-fee readiness assessment scores your gaps and picks the highest-value use case. Then a time-boxed pilot proves trusted answers on your own stack - before you scale.