`DOCUMENT AI & DATA`
Documents in, validated records out.
Pipelines that take inbound documents and attachments, extract and validate the content with AI, and sync it to your systems of record. Analysis-ready instead of re-keyed.
Start a conversationWhat we build
AI document extraction and validation
Event-driven ingestion from email, portals and APIs
Sync to policy, claims, CRM, rating and core systems
Data pipelines on Databricks and Spark
Industry formats: ACORD, EDI, HL7 / FHIR
What we own in production
Extraction accuracy tracked against an evaluation set
Exceptions routed to a human reviewer
Every record logged and traceable
Private-tenant deployment with models called in-region
Typical team shape
** PM, solution architect, 2 back-end engineers, data / AI engineer, QA.
Related work
All work →Submission intake and claims integration for a US commercial insurer
An event-driven layer that turns inbound ACORD submissions and attachments into underwriter-ready data instead of PDFs to re-key.
Claims and national registries for a mandatory health-insurance system
A reimbursement engine, a national registry of insured members and a master-data hub with 200+ dictionaries.
Data-aggregation platform for 32,000 users
Ingests data from many web sources and resolves it into one unified record.
QUESTIONS
Frequently asked questions
What accuracy should we expect?
We agree the target on your documents before building, then measure against an evaluation set built from them. We do not quote a number in advance.
What happens to documents the model is unsure about?
They go to a person, with the uncertain fields highlighted.
Can it run in our cloud?
Yes. Our insurance work runs in the client's private Azure tenant, VNet-isolated and region-locked.
START
One line is enough.
Tell us what is slowing your engineering down. We reply with what we would do about it.