Motor claim photo assessment
Damage detected from photos, estimate built from parts and labour, adjuster only approves; repair slot booked.
Motor claim photo assessment
- 6 photos received on WhatsApp for claim #CL-2026-118342
- Image quality sufficient, EXIF date matches the incident, no reused images
- Damage: front-right wing + bumper, medium depth, headlamp intact
- Plan: parts/labour → estimate → threshold check → repair routing
- Prices: wing 4,200, bumper 6,800, paint 3.5 hours
- Estimated cost 19,600 (± 12%), not a total loss, excess 2,000
- Estimate > 15,000 → adjuster approved in 3 minutes, one line item corrected
- 3 partner shops within 5 km, earliest slot tomorrow 14:00
- Estimate and repair booking written to the claim, reserve updated to 19,600
- WhatsApp to customer: estimate summary, shop address and appointment
Simulation · derived from real agent definitions · every agent can be built by dialogue with the Autonomous Agent and validated with a test corpus
Photos uploaded to an open claim via WhatsApp or the app
Approved estimate and reserve on the claim, repair slot booked, customer informed
Agents
Claims Supervisor
Decomposes the objective, delegates to agents, manages approval points, merges the result.
Vision Agent
Assesses photo quality and damage, checks for reuse or manipulation
check_image_qualitydetect_damageestimate_severitydetect_reuseEstimate Agent
Builds the estimate from parts and labour prices
get_parts_pricesget_labour_ratesbuild_estimateRepair Routing Agent
Finds a partner shop, books the slot, updates the claim
find_repair_shopsbook_slotupdate_claimsend_whatsappSteps
| # | Kind | Agent | What happens | System | ms | tok |
|---|---|---|---|---|---|---|
| 01 | ingest | Claims Supervisor | 6 photos received on WhatsApp for claim #CL-2026-118342 | — | 240 | — |
| 02 | verify | Vision Agent | Image quality sufficient, EXIF date matches the incident, no reused images | — | 620 | 540 |
| 03 | reason | Vision Agent | Damage: front-right wing + bumper, medium depth, headlamp intact | — | 1,200 | 1,650 |
| 04 | plan | Claims Supervisor | Plan: parts/labour → estimate → threshold check → repair routing | — | 280 | 190 |
| 05 | tool | Estimate Agent | Prices: wing 4,200, bumper 6,800, paint 3.5 hours | Parts & Labour Catalogue (Audatex) | 460 | — |
| 06 | reason | Estimate Agent | Estimated cost 19,600 (± 12%), not a total loss, excess 2,000 | — | 780 | 720 |
| 07 | approval | Claims Supervisor | Estimate > 15,000 → adjuster approved in 3 minutes, one line item corrected | — | 2,900 | — |
| 08 | tool | Repair Routing Agent | 3 partner shops within 5 km, earliest slot tomorrow 14:00 | Repair Network Portal | 380 | — |
| 09 | write | Repair Routing Agent | Estimate and repair booking written to the claim, reserve updated to 19,600 | Guidewire ClaimCenter | 520 | — |
| 10 | notify | Repair Routing Agent | WhatsApp to customer: estimate summary, shop address and appointment | WhatsApp Business API | 260 | — |
Voice claims intake (FNOL)
The customer calls and describes it; policy found by plate, cover checked, claim opened and adjuster assigned.
Policy renewal offer
Policies re-priced, sharp increases go to approval; customer accepts with one tap and the policy is issued.
Underwriting document extraction
Medical reports and labs read and assessed against the manual; underwriters only see files above threshold.
Time to move from experimenting with AI to transforming with it.
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- Your 2–3 priority problems
- Live demo of a similar scenario
- Roadmap starting with value analysis