Insurance-native AI. Built for the complex workflows of insurance.
Kapability AI’s insurance-tuned LLMs assist claims and underwriting processes by parsing multi-page adjuster reports, medical records and policy contracts on-premise. Turnaround times fall without changing a line of core infrastructure code, and final authority stays with your claims handlers and underwriters.
4. Findings
The vehicle was inspected at the contracted workshop on 16 September 2026. Impact damage was found on the front right corner, affecting the bumper, headlamp unit and hood. The windshield is cracked across the passenger side.
The third-party driver accepted full liability at the scene. A signed accident report is attached as Appendix B.
The workshop estimate totals €8,420.00 including labor, of which €1,810.00 relates to glazing.
5. Recommendation
Repair is economical. No signs of earlier damage were found on the affected panels, and the parts listed match the impact pattern described by the insured.
The workshop has confirmed availability of the headlamp unit. Expected repair time is six working days.
- Date of loss
- 14 Sep 2026 p. 1
- Damage
- Front right corner, windshield p. 3
- Liability
- Third party, 100% p. 3
- Repair estimate
- €8,420.00 p. 3
- Glazing
- €1,810.00 p. 3
- Deductible
- €250.00 Policy 2.1
- Collision damage coveredBumper, headlamp, hoodClause 3.1
- Glazing over sub-limit by €310€1,810 claimed, €1,500 limitClause 3.4
- Recourse possibleThird party admitted liabilityClause 7.2
Illustrative interface. Screens and data shown are representative.
What Kapability guarantees
Insurance domain-tuned
Fine-tuned for all documents related to insurance workflows, such as adjuster reports, medical files and policy wordings. Deductibles, sub limits and exclusions are understood from day one.
Private on-premise execution
Runs inside your corporate network. No external API exposure, no cloud use and no data used to train outside models.
Non-invasive core overlay
An intelligence layer above your existing databases and core ledgers. Live in weeks, without core code rewrites or a multi-year modernization program.
Expert human-in-the-loop
AI handles the paperwork: parsing documents and extracting discrepancies. Your experts focus on the critical decisions, such as claim approvals, payouts and risk acceptance.
The problem
Claims handlers and underwriters aren’t short of judgement: they’re buried in unstructured documents. Free text adjuster notes, line item repair estimates, handwritten physician notes and discharge summaries, read and handled manually, case after case.
The claims bottleneck
Adjuster reports mix free text notes, line item estimates and liability descriptions. Cross checking them against policy clauses is manual data extraction, and it shows up as long TATs from FNOL to payout, claims leakage and fraud.
The underwriting drag
Life and health underwriters spend up to 40% of the day in physician notes, lab forms and discharge summaries, looking for chronic risk factors. Policy approvals wait behind the reading.
The core system dilemma
Replacing a legacy core ledger takes years, costs millions and puts operations at risk. Document automation needs a non-invasive overlay on private infrastructure, not a core overhaul.
Specialized workflow engines, one per desk.
Each engine is fine tuned on the department’s documents, terminology and rules across insurance workflows, and deployed module by module. Start with the desk carrying the longest backlog and extend from there.
Claims automation engine: motor, non-motor and health
Loss adjuster reports in, checked claim files out.
Agentic First Notice of Loss (FNOL): parses adjuster documents, accident report forms and damage reports, cross-matches every finding against the active policy and pre-triages the claim before a handler even opens the file.
- Adjuster report parsing. Structured data fields, line-item repair estimates, deductibles and liability notes.
- Policy and clause cross matching. Coverage limits, sub limits, exclusions and clause discrepancies flagged before payout.
- Recourse and fraud signals. Subrogation potential and inconsistencies between documents raised for the claims handler.
| Line | Amount | Policy check |
|---|---|---|
| Front bumper, replace | €1,240.00 | Covered |
| Headlamp unit, right | €960.00 | Covered |
| Hood, repair and paint | €1,690.00 | Covered |
| Windshield, replace | €1,810.00 | €310 over sub-limit |
| Labor, 14 hours | €1,260.00 | Covered |
| Paint materials and sundries | €1,460.00 | Covered |
| Total claimed | €8,420.00 |
Recourse possible. The third party admitted liability (adjuster report, p. 3).
Illustrative interface. Screens and data shown are representative.
Medical underwriting engine: life and health applications
Clinical documents read, findings mapped to your risk register.
Parses unstructured physician notes, lab results and hospital discharge summaries for chronic risk factors and clinical indicators, and maps each finding to your internal underwriting guidelines.
- Clinical document intelligence. Diagnoses, medication, lab values and dates, each linked to its source page.
- Risk register mapping. Findings matched to the guideline and rating rule that applies with possible non-disclosure flagged.
- Underwriter authority. Risk acceptance and rating decisions stay with the underwriter.
| Finding | Source | Guideline |
|---|---|---|
| Type 2 diabetesDiagnosed 2019, HbA1c 7.1% | Lab report, p. 4 | M-12, refer |
| HypertensionControlled, 132/84 mmHg | Physician note, p. 2 | C-04, standard |
| Non-smokerCotinine test negative | Lab report, p. 6 | Confirmed |
| BMI 29.4178 cm, 93 kg | Medical exam, p. 1 | B-02, within table |
Decision: underwriter. Suggested next step is a referral under guideline M-12.
Illustrative interface. Screens and data shown are representative.
Field & portfolio operations engine: agency and broker channel
Retention signals before the renewal window opens.
Analyzes customer profiles and portfolio data across property, motor and health lines, and prepares briefs for field managers ahead of agency and broker visits.
- Renewal retention signals. Policy lapse risk flagged well ahead of the renewal window.
- Cross-sell opportunities. The next relevant cover for each policyholder, based on profile and portfolio.
- Agency and broker enablement. Portfolio summary briefs and discussion points ready before each visit.
| Policy | Renews | Signal |
|---|---|---|
| Fleet, 42 vehiclesMotor | 12 Dec | Lapse risk high |
| Home and contentsProperty | 19 Dec | Offer earthquake cover |
| SME packageProperty | 3 Jan | Broker changed |
| Family healthHealth | 8 Jan | Dependants turning 18 |
Visit brief ready for 14 Nov. Four renewals to discuss and two cross-sell openings.
Illustrative interface. Screens and data shown are representative.
From document ingestion to a decision-ready claim file.
One motor claim, followed through the pipeline from first notice of loss (FNOL). Medical underwriting files run through the same four stages.
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Ingestion above your core
Connectors sit on top of your policy administration system and claims system and pick up new files from FNOL onward. No core code changes, no data migration.
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Domain-tuned extraction
Kapability AI’s insurance-tuned language models read scanned and native documents: structured fields, line items, clinical indicators and liability notes.
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Clause cross-matching
Findings are checked against the policy wording, endorsements and guidelines. Coverage limits, deductibles, exclusions and subrogation potential are resolved before payout authorization.
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Human-in-the-loop decision
The claims handler reviews a prepared file, verifies any value at its source and approves or returns it. Review cycles and turnaround times (TAT) compress from days to minutes.
Built for strict regulatory and risk environments.
Models execute inside your corporate network, on infrastructure you control. Operational data is never sent to commercial APIs or cloud services, or used for third-party model training, so data sovereignty stays with you.
- Zero external API calls
- Zero cloud use
- Zero core changes
Private infrastructure deployment
On-premise, with no external API exposure and no cloud use. Your data never trains anyone else’s model.
Non-invasive overlay architecture
Reads from existing ledgers and databases. No core rewrites, no costly migrations, no risk to core system stability, and light to maintain.
Verifiable traceability
Every extracted data point links to the exact page and paragraph in the source file, giving auditors a complete decision trail.
Human-in-the-loop governance
Payouts, policy approvals and risk acceptances remain under human expert authority, limiting legal liability and operational risk.
Designed for GDPR, DORA (Digital Operational Resilience Act) and banking secrecy requirements, with role-based access control and logging under your own policies.
Feasibility before fine-tuning.
We work inside one sub-department at a time. If a rules engine or classical machine learning does the job better than an LLM, the feasibility analysis says so.
Process discovery
We sit with the sub-department, whether claims, underwriting or sales, and map where manual document work consumes handling time.
Feasibility analysis
Each task is tested against an LLM, classical ML and rules. Only what passes moves to build.
Fine-tuning
Foundation models are fine-tuned on our proprietary insurance and banking datasets, then on your data, on your servers.
Domain validation
Each module is tested against your document formats, policy clauses and underwriting guidelines before production.
Module-by-module deployment
Production-ready document parsing in weeks, measured on TAT and leakage before the next module starts.
Questions we hear from insurers.
Something else on your mind? Ask us directly.
Does any operational data leave our network?
No. Models run on your servers. Storage, inference and fine-tuning happen inside your perimeter, with no calls to commercial AI APIs or cloud services, and nothing is used to train external models.
Do we have to change our core system?
No. Kapability AI is a non-invasive overlay above your core ledgers and databases. It reads through the access you grant; your core code and its stability are untouched.
Which models do you use?
Foundation models suited to on-premise deployment, fine-tuned on our proprietary insurance and banking datasets and then on your data inside your environment. Model selection is part of the feasibility analysis.
What does a technical PoC involve?
We run Kapability AI on sample loss reports or clinical files under NDA and evaluate it against your guidelines, policy clauses and document formats. It never connects to production core ledgers.
Do you work with banks?
Yes. The same approach covers document-heavy banking operations, such as credit and KYC files, under banking secrecy rules.
Evaluate it on your actual files.
Schedule a technical proof of concept tailored to your claims and underwriting workflows. We parse your multi-page loss adjuster reports, medical files and policy contracts inside your private environment.
- Zero infrastructure disruption. Run on sample loss reports or clinical files, never connected to production core ledgers.
- Domain validation. Measured against your guidelines, policy clauses and document formats.
- Private evaluation. Conducted under NDA, with full data privacy.
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