Incident details stay together
Link the customer’s account of the incident and the relevant case references.
Claims automation · AI workforce
Give your claims team a complete starting point.
Intake captured, documents collected and gaps chased, so an assessor opens a referenced file rather than a blank one.
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What better looks like
Start with the result your team needs.
These are the intended outcomes to validate for your deployment. Judge the employee by the work completed and the exceptions handled, not by conversation volume alone.
Missing items become visible work
Maintain a checklist and follow up on outstanding information within the configured scope.
Handlers receive review-ready intake
Prepare the packet with unresolved questions clearly flagged, not a promise of claim approval.
The evidence standard
A prepared intake packet is evidence of progress, not approval.
Measure the pilot against agreed completion criteria and verify each outcome in your systems.
Claims intake packet: An intake packet ready for the claims handler.
- 01
Incident linked
Details saved against the correct case
- 02
Checklist reconciled
Received and outstanding items distinguished
- 03
Review packet prepared
Evidence and open questions sent to the review queue
Intake readiness does not establish document validity, coverage or settlement approval.
| Outcome to validate | Evidence that supports it | What is not sufficient |
|---|---|---|
| Captured incident | Recorded incident details and a matched case or policy reference. | A conversation completed without a linked record. |
| Document readiness | Checklist with received and outstanding items explicitly identified. | An upload treated as validated evidence. |
| Ready for review | Saved packet, open questions and an assigned review queue. | Intake completion presented as accepted coverage or settlement. |
Your baseline
Compare against your own baseline.
Define review-ready intake with claims handlers before the pilot. Measure missing-information cycles, time to that state, incorrect case updates and unowned exceptions. Report claims decisions and settlement outcomes separately from intake performance.
The workflow
See the responsibility in action.
See the employee’s actions, the record it prepares and when a person takes ownership.
Aisha · AI employee
From first notice to a review-ready claim
Conversation
My car was damaged yesterday. I need to start a claim.
Claim intake
- Incident · details recorded
- Policy · reference matched
- Documents · checklist prepared
Claims + documentsCheck outstanding documents
Illustrative scenario, not live product data.
Put a role behind the outcome
Hire Aisha. Or build your own.
Choose a pre-built role, then configure its knowledge, supported actions and limits for your environment.
AI Claims Intake Agent
Aisha
Capture incident details, collect outstanding items and prepare a referenced intake packet.
Custom role
Build a custom workforce
Design a responsibility around your outcome, systems and exception rules. Agree the evidence that will establish completion before adding channels or expanding autonomy.
Make the outcome supportable
Agree the access, authority and evidence.
A credible pilot needs the right source records and clear boundaries. Review the proposed scope with the owners who will operate and assess it.
Connect the source of truth
Scope claims records, policy references, document storage and handler queues. Confirm matching, authorized access, checklist versions and the difference between receipt and validation.
Keep decisions with the right owner
Coverage questions, liability, document assessment and settlement remain with the claims team. Missing or conflicting evidence should be flagged for review, not silently resolved through inference.
Before the first call
Bring evidence to the first discussion.
Share anonymized examples, the current outcome definition, baseline measurements and cases that went wrong. Agree the test cohort, observation window and release criteria. Keep unresolved and failed cases visible in the evaluation.
Claims automation questions
Know the outcome,
and its limits.
AI claims automation is a workflow an AI employee owns from first notification through document collection and gap chasing, so an assessor opens a referenced file rather than a blank one.
No. The example covers intake preparation: capturing details, linking records, following up on missing items and preparing the packet. Coverage and settlement decisions remain outside Aisha’s defined responsibility.
Agree a checklist with the handlers for the claim type in scope. Required details should be linked, document states explicit and unresolved questions visible. Review-ready does not mean every document is valid or the loss is covered.
Receipt and validation are different events. The role can record receipt and organize the packet, but any required document assessment needs its own authorized process and evidence.
Compare similar claim types using agreed readiness criteria and observation periods. Measure missing-item cycles, time to readiness and errors in the linked record. Avoid presenting faster intake as faster settlement without separate supporting results.
Agree a baseline and completion criteria for the workflow. Compare outcomes using your source records, including verified system changes, human review and unresolved cases.
Both. Start with a pre-built responsibility shown here, or design a custom role around your outcome. In either case, scope knowledge, system access, permitted actions and exception ownership before launch.
Bring the target outcome, baseline records, representative requests and exceptions, required systems and an accountable owner. Agree success criteria and data-handling requirements before testing. Timing depends on access, integration and review work rather than a universal launch promise.
Confirm the specific systems, API operations, access requirements and channels with the implementation team. The categories here are a scoping guide, not a native-integration guarantee. Test failure handling and result verification as well as successful interactions.
Customer reviews
From conversations to completed work.
Voice AI for client work
“Using UnleashX gave our team the power to build voice AI agents that deliver real results for our clients. Every use case became faster to deploy and easier to scale.”
What would a better outcomelook like for your team?
Bring one responsibility and the records that define success. We’ll scope the employee and the evidence needed to evaluate it.