- Claim arrives
- Classify docs
- Extract fields
- Check cover
- Pack ready
- Handler decides
The decision takes a minute. The evidence takes a week.
Claims, credit files, KYC packs and policy wordings arrive in every format there is. Exemplary AI does the reading inside your own network and hands the reviewer a decision-ready pack, every value pointing at the page behind it.
None of this is the decision.
The decision is usually easy. It is everything around it that takes the week.
Intake is a format problem.
One motor claim: a phone photo and a posted form. One credit file: six months of statements in three layouts. One merchant application: whatever the applicant had to hand.
Verification means re-reading.
Every check, whether it is cover confirmation, source of funds, beneficial ownership or document completeness, starts with someone opening files a colleague already read last week.
The queue is where the cost is.
Almost no step in the process is difficult. What the process is, is serial: each step waits for a person to reach it, and the waiting is most of the elapsed time.
The audit asks why.
Six months later a regulator, an internal auditor or an ombudsman wants the reasoning behind a decision. If that reasoning lived in one reviewer’s head, the reconstruction is its own project.
Claims, onboarding, monitoring. One underlying job.
Read a document, check it against the rule that applies, and show your working. What changes between the three is which rule, and whose document.
Read the submission, assemble the evidence.
The agent identifies each document in a submission - motor claim, chargeback pack or credit file - pulls the fields the reviewer needs, and checks them against the wording or policy that applies. What comes back is a pack where every line points at the page it came from.
- Document classification across photos, scans, portal uploads and email
- Extraction with 99.2% accuracy on the document types it is set up for
- Checked against the wording that governs the case, with the clause cited
“Is windscreen damage covered on this policy?”
Covered — with an excess
Policy wording · clause 7.2 · p.14Retrieved from your own indexed wordings, not from general knowledge.
A motor claim lands at 09:12.
The panel is the handler’s view. Four things happen to the file before anyone opens it, and the fourth is where a person takes over. A chargeback pack and a credit file take the same route.
In the submission
- Damage photographs×2photo
- Repair estimate×1estimate
- Police report×1report
- Policy schedule×1schedule
- Covering email×1letter
Read out of it
- Incident date
- 11 Marreport
- Vehicle
- matches the scheduleschedule
- Claimed amount
- under limitestimate
- Third party
- identifiedreport
Cover check
Covered, with an excess
Policy wording · clause 7.2 · p.14Ready for the handler · 09:16
- Excess still to confirm with the customer
- Estimate lists a panel the photographs do not show
Every value links back to the document it was read from.
IllustrativeTimes are illustrative. They show the shape of a morning, not a benchmark.
- 09:12
Everything in the submission is identified.
Six attachments: two damage photographs, a repair estimate, a police report, a policy schedule and a covering email. Each is classified before anything is extracted, because the extraction rules differ by document type.
- 09:13
The fields the handler needs are pulled.
Policy number, incident date, vehicle, claimed amount, third-party details. Each value keeps a link to the page and position it was read from, so verification is a glance rather than a re-read.
- Cross-checked between documents
- Conflicts surfaced, not silently resolved
- 09:14
Cover is checked against the wording that applies.
Grounded retrieval over your own indexed policy documents — not a general model's idea of what insurance usually says. The relevant clause comes back with the answer.
- 09:16
The handler gets a decision-ready pack.
A summary, the extracted values, the clause, and the open questions. The agent recommends; the handler decides; the audit trail records both. The pack is opened at 09:41, when the handler reaches it: this time the file waited for the person rather than the person for the file.
When every file has been read, you can ask the book a question.
The first thing anyone notices is the week back on a single file. What changes the job is what exists a quarter later. Every claim, credit file, onboarding pack and policy wording has been read, linked and cited, so a question about the whole book is a question with an answer.
“Which open files rely on the wording we changed in March?” stops being a sampling exercise and becomes a list of files.
A catastrophe week or an onboarding surge lands on a queue no longer waiting for a person to reach each file.
The experienced reviewers spend the day on the exceptions, the borderline cover calls and the customers who are genuinely stuck.
Customer records stay on your side of the firewall.
Financial data has a residency story, a retention story and an audit story. A cloud API endpoint has none of them.
Inference happens inside your perimeter
Bare-metal, private cloud, or fully air-gapped. No document, extracted field or embedding is sent to a model provider — including for the models you already license.
Residency and retention you set
AES-256 at rest, TLS 1.3 in transit, and data that never leaves the jurisdiction you deployed it in. Access is role-based, with deny overriding allow.
An audit trail that answers 'why'
Every retrieval, extraction and recommendation is logged with the documents behind it — so the reasoning behind a decision survives the person who made it.
The frameworks your own auditors already ask about, held by the deployment your records never leave.
Configured for claims, credit files and onboarding packs. Not rebuilt for any of them.
The same platform the rest of the business runs, which is why the fraud team, the complaints desk, the credit committee and the actuarial archive can all sit on it too.
- Intake
- Filestore folders, watched sync, upload or API
- Reading
- OCR, indexing, entity recognition and linking
- Checking
- Grounded retrieval against your own wordings, policies and rulebooks
- Structure
- Bases and Data Tables for extracted values
- Monitoring
- Scheduled automations with run history
- Access
- Groups and IAM — least privilege, deny overrides allow
- Systems
- 170+ integrations via MCP, plus custom MCP servers
- Deployment
- On-premise, private cloud or air-gapped
- Updates
- Offline model updates by secure transfer
What operations, risk and audit teams ask first.
See Exemplary AI in action
Book a demo and we'll show you purpose-built agents grounded in your own knowledge — deployed on your infrastructure.