Your AI is only as good as your worst spreadsheet.
Every financial firm runs on data that disagrees with itself. Positions in one system, prices in another, documents everywhere, plus a month-end process that papers over the gaps. The Data Engine fixes the layer beneath, so everything built on it can be trusted by your AI and by your auditors.
Most AI failures are data failures wearing a disguise.
When an AI system hallucinates a position or misstates an exposure, the model usually gets the blame. The cause is almost always context: data that was stale, duplicated, or quietly contradictory.
That is why Mezza builds the data layer first. An intelligence layer on ungoverned data is just confidence without grounding.
Ingest. Reconcile. Govern.
Ingest
Connectors for the systems financial firms actually run: portfolio accounting, custodians, market data feeds, document stores, and internal databases. Structured and unstructured sources land in one place, with their origin recorded at entry.
Reconcile
The hard part. When two systems disagree on a position, a price, or an entity, the engine does not silently pick one. It resolves what it can by rule, flags what it cannot, and keeps the disagreement on the record. Entities are matched across sources, so “the same counterparty under three names” becomes one counterparty.
Govern
Every field carries lineage: where it came from, how it was transformed, who can see it. Entitlements follow your access model.
One version of the truth, with receipts.
One reconciled layer
Research, risk, and reporting read from it. No more “whose number is right” meetings.
Flagged exceptions
Discrepancies surface daily, not quarterly as audit findings.
New sources
They join through the same pipeline, governed from day one instead of bolted on.
Lineage already written
When a regulator or an LP asks where a number came from, the answer is one click deep, not a forensic project.
Deploy it first on its own, or as the base layer of the full platform.
Drakkar platform overviewBring us your messiest reconciliation.
The fastest way to evaluate a data engine is to point it at a real problem. In a demo, we will walk a workflow like yours through ingest, reconciliation, and lineage, step by step.