For an insurance operations team, receiving a document was only the start of the work. Staff still had to identify what it contained, enter the relevant information, check it, and move it to the right system. This engagement connected those steps into a document intake workflow, with AI assistance for classification and extraction and explicit review points for exceptions.
What changed for the people using it
- Before
- Operations staff interpreted incoming documents and transferred information between intake and downstream systems by hand.
- After
- A connected pipeline classified and extracted information, applied validation, and routed exceptions for staff review before downstream use.
The contribution at a glance
- Our role
- Workflow discovery, AI pipeline design, implementation, and integration.
- Work delivered
- Document classification and extraction, validation, exception review, downstream APIs, and operating documentation.
01
Incoming documents
Identify document classes and the fields each operational workflow needs.
02
Extraction & review
Apply AI assistance, validation, and human review for exceptions.
03
Operational systems
Route validated information to policy and claims applications.
Scope & current status
An anonymized insurance delivery account covering the intake workflow and its handoff to operations.
The challenge
Policy documents, claims packages, endorsements, and supporting exhibits arrived in different formats. A single extraction rule could not cover the variety, and a field that looked plausible still needed to satisfy the receiving system's requirements.
The people handling intake needed to know which documents could move forward and which required attention. The engineering problem included the surrounding workflow: incomplete fields, inconsistent information, review queues, and integration with existing policy and claims applications.
The approach
The work began by mapping document classes, required fields, validation rules, and the destination of each output with operations and compliance stakeholders. That made the scope concrete before selecting where AI could assist.
The implementation separated ingestion, classification, extraction, validation, exception handling, and API routing. AI-produced fields passed through validation rather than going directly into an operational record. The rollout began with a bounded document class and expanded through parallel review of the new and existing workflows.
The work in detail
- 01
Map the complete intake job
Connect each document class to the information staff needed, the checks they performed, and the system that ultimately consumed the result.
- 02
Build the extraction pipeline
Implement classification and AI-assisted extraction alongside deterministic handling for suitable structured formats.
- 03
Give exceptions somewhere to go
Apply document-specific validation and route exceptions to a human review queue with processing history available for inspection.
- 04
Integrate and hand over
Connect validated outputs to policy and claims APIs, introduce the workflow in stages, and document exception handling for the operations team.
The outcome
The delivered workflow connected document intake to downstream operational systems and made exception review part of the processing path. Staff had a defined place to inspect work that needed judgment, while classification, extraction, and routing were handled within the pipeline. Operating documentation and staff handoff supported the transition into daily use.
Why it matters
The useful unit of automation was the intake job people needed to finish. Extraction became operationally useful when it was connected to validation, review, and the receiving system. That is the work required to move from an AI demonstration to a workflow an operations team can use and manage.