Insurance Data and AI Your Regulator Can Trust
Bring policy, claims and customer data into one trusted layer. Run AI privately, close to that data. Keep a record of every model and every decision.
The problem
AI pilots stall on data and trust
Most insurers have tried AI. Few have it in production. The data is spread across the core, claims, CRM and spreadsheets. Legal teams worry about customer data leaving the country. Regulators now ask how each model was tested and who signed it off.
What we build
The data, AI and controls, together
We fix only the data feeds the first use case needs. Then we run one AI use case in production, with its controls in place from day one. Each new use case reuses the same layers.
01
AI use cases
Claims triage, underwriting assistant, service agents
02
Trusted data layer
Policy, claims, customer and finance data in one place
03
Your systems
Core, claims, CRM, finance and partner feeds
Governance across all layers
Model inventory · testing records · decision logs · access control
Proof
AI we run in production today
93.3%
RAG retrieval accuracy
At a processing cost of $0.18 for the full benchmark corpus.
14.5 months
AI in production
99.99% of applications scored by AI and reviewed by people, on our AI recruitment platform.
Related solutions
What regulators are asking for
US
Many state insurance regulators have adopted the NAIC (National Association of Insurance Commissioners) model bulletin on AI. It expects a written AI programme and testing records.
GCC and India
Customer and health data is often expected to stay in-country. Private AI keeps it there.
Capabilities used
Data Engineering
Data Warehouse
Data Analytics
Big Data Consulting
Enterprise AI Platform
RAG Development
AI Consulting
AI Agents
FAQ
Questions about data and AI
No. We build only the data feeds the first use case needs, then extend.
Yes. Models can run privately in your cloud region or data centre.
A list of every model, how it was tested, who approved it, and a log of the decisions it supported.
Yes. We feed your current BI (business intelligence) dashboards, or set up new ones if needed.
Get your first AI use case into production
Start with a data residency check and one use case.
