`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.
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Tell us what you need to build or improve, and what the solution has to work with. We will reply with a practical next step.
GOOD TO KNOW
Frequently asked questions
What does a document AI solution do?
It turns incoming documents into validated, structured information that a business process can use, with exceptions routed for review.
Can you process insurance submissions?
Our published US insurer case describes AI-assisted intake that reads submissions, checks extracted data and routes exceptions before downstream systems are updated.
How do you measure extraction quality?
A representative evaluation set and explicit validation rules help test accuracy, edge cases and regressions as the pipeline changes.
What happens when a document cannot be parsed confidently?
The workflow can hold the record and route it to a human reviewer instead of silently passing uncertain data to another system.
Do you integrate the output with existing systems?
Yes. Structured results can be connected to case management, claims, records or other downstream applications through agreed interfaces.
Can you modernize an existing data pipeline?
Yes. We can map the current sources and consumers, improve ingestion and validation, and migrate or replace fragile components in stages.
Have you worked at large data scale?
Our published metropolitan records case describes migrating 200 TB of historical records into a new long-term platform.
What data controls matter in regulated workflows?
Access, traceability, retention and residency are defined against the client’s environment and applicable requirements. Human review may be needed for sensitive decisions.
Does the work include non-AI data engineering?
Yes. Data ingestion, transformation, master data, migration and integration can be delivered independently of AI models.
What information helps scope a document pipeline?
Share sample document types, volumes, required fields, exception rates, destination systems and security constraints. We can map the pipeline from there.