Data Hatch
Four weeks embedded with a ~€300M specialty carrier: every leader interviewed, the platform reviewed end to end, a working fraud agent on the table.
Dashed figures are placeholders pending confirmed engagement data.
The client writes roughly €300M in gross written premium across a deliberately fragmented portfolio: aviation, marine, and dozens of highly customised, often one-of-a-kind products. Low frequency, high severity claims. Small data volumes per line. The hardest possible statistical terrain for AI, and a business already investing seriously in it.
Like most organisations moving fast on AI, each new use case was being wired directly into source systems rather than into a governed data layer. Every project paid a full integration tax: slow to build, impossible to reuse, and disconnected from the data governance discipline the business had invested in. There was no standard way for agents, regardless of which model powered them, to discover and consume governed data products.
Leadership had a clear three-layer ambition: fix the foundations, build a governance and context layer, then scale business process automation on top. What they needed was an outside view on whether their architecture would survive that journey without a redesign.
Not a foreign framework: an extension of the client’s existing governance discipline to models and agents, in three moves.
Existing data products, lineage, and contracts brought into an open, standards-based catalog, and keeping the governance discipline the business already invested in.
Any agent, on any model, discovers and consumes governed data products through one interface. The per-project integration tax ends here.
Fraud detection taken from prototype to production on top of the governed layer, with generation-then-governance controls for auditability.
Built in a day and a half and demoed to the leadership team: the agent hops the claim graph entity by entity, ensembles LLM reasoning with traditional models, and writes its rationale on every decision.
A replay of the prototype demo. Nothing here runs in production; taking it there is step three of the plan.
CEO, CIO, and the heads of data engineering, digital innovation, and data science signed off a single target architecture.
From lakehouse to legacy policy systems: every surface the agents will touch, assessed in writing.
The MCP layer replaces per-project integrations with one governed way in, on any model.
Govern, expose, deliver: a plan the client’s own team can execute, budget line by budget line.
The target architecture extends the client’s existing governance investment. Nothing gets thrown away.
The fraud agent turned an abstract architecture debate into a demo the leadership team could argue with.
Dashed figures are placeholders pending confirmed engagement data.
Most insurers are being sold AI use cases one integration at a time. This engagement shows the alternative: invest once in a governed context layer, and every subsequent agent (underwriting, claims, fraud, or pricing) plugs into the same governed foundation. Faster to ship, reusable by design, and auditable end to end. That is the architecture Data Hatch designs and builds for regulated industries.