Claims adjudicationas reconciliation, not reading.

A source-grounded workspace for claims adjusters and underwriters: classify, extract, and defend at portfolio scale.

No credit card. Regulator-defensible by architecture.

A claim package and policy reconciling into a structured coverage decision

Adjudication isn't reading. It's reconciling.

The claim has six documents. Each says something slightly different. The policy has seven amendments. Your job is to reconcile what happened against what's covered. Reading is the unavoidable scaffold; reconciliation is the work.

Most AI for documents helps with the scaffold. The work needs different tools.

5M+ Documents analyzed
30+ Models, one interface

Claims package in, structured decision out.

Drop a claims package. Pull the relevant docs via Smart Search & Selection, extract policy-relevant fields via Data Extractor, run Individual workflow per supporting document. The output is a structured decision card, not another chat transcript.

Claims package classified, field-extracted, and structured into a decision card

Workflows adjusters ship

Built for the adjudication cycle.

Smart Search & Selection

Describe what you're looking for ("Auto claims under $10k", "Workers comp filings missing physician notes") and pull matching documents into a working set. Reduce triage time, route to specialists faster.

Data Extractor

Pull structured fields from policies, claims, and supporting documents. Output is a spreadsheet, cited back to source: the foundation of a defensible decision.

Individual

Ask the same coverage question of every supporting document. Get per-document answers in a structured layout, not a wall of prose.

What insurers push on hardest.

  • Regulator defensibility: every decision must trace.

    Cited answers are clickable, opening source at the right page. Extracted fields trace cell-by-cell back to the document they came from. The audit trail is the citation system, not a separate log.

  • Policyholder data sensitivity is regulated.

    Per-tenant isolation. No training on customer content. Session-scoped artifacts. Your data architecture, not ours.

  • We have policies and claims in 12 formats across our portfolio.

    Documents handled consistently regardless of input format: PDF, Word, scanned, mixed exhibits all handled. The same workflows work across the portfolio.

Frequently Asked Questions

Can I pull out the claims I need to work on?

Smart Search and Selection takes a description, such as auto claims under a threshold or a particular workers compensation pattern, and assembles that set for you.

Can it extract fields from policies and claim files?

Yes. The Data Extractor pulls structured fields from policies, claims and supporting documents into one table, across the whole set in a single pass.

Can I ask the same coverage question of every supporting document?

Individual does exactly that and returns a per-document answer, so you can see which document supports the position and which one contradicts it.

Can a coverage decision be defended later?

Each cited claim opens the source at the exact page, so the basis for a decision stays attached to the decision instead of living in someone's memory.

Is claimant material kept confidential?

It is never used to train models and is isolated per tenant. The security page sets out retention, deletion and every vendor in the path.

How large a file can a claim bundle be?

Up to 500 MB per file depending on plan, and a dataset can hold the whole bundle rather than one document per conversation.

Pilot on a single line of business.

Upload a recent claims package, define your standard extraction schema. Compare the structured output to what an adjuster produces manually today.