Overview:
Picture this: you get rear-ended, adrenaline’s spiking, your brain’s mush. You open one app and it calmly tells you exactly what to do: snap these 6 photos from these angles, record a quick voice note from the witness (with consent), auto-grab time/GPS, it sketches a clean diagram of the scene for you, then spits out a tidy, insurer-ready claim packet and tracks the back-and-forth with the adjuster. Basically a claims sherpa that turns chaos into structured evidence, so you don’t forget something important and get lowballed later.
The Trends:
AI-first claims automation: carriers are deploying generative AI and ML to triage, draft communications, estimate damage, and automate routine claim tasks, reducing cycle time and scaling adjuster capacity. (1, 2)
Mobile-first evidence capture & standardized checklists: carriers and vendors emphasize smartphone photo capture, guided workflows and standardized evidence checklists to improve claim accuracy and speed settlements. (3, 2)
Cloud-native claims platforms and API integrations: cloud adoption and modular, API-driven platforms let apps connect to carriers, telematics, repair shops and adjusters for real‑time status and automated handoffs. (4, 3)
Data fusion for fraud detection and loss estimation: carriers combine images, telematics, historical claims, and public data with ML to detect fraud, refine estimates and prioritize high-value investigations. (1, 2)
Customer experience & trust architecture drive adoption: poor claims experiences are a key churn risk, so carriers invest in empathetic automated communications, privacy/trust controls, and transparent evidence-handling to retain customers. (5, 1)
Your Answer:
What it is: a post-accident mobile assistant that walks users step-by-step through evidence capture (photos with angle prompts, video, geotagged witness details), auto-generates a clear accident diagram, drafts the insurance claim, and tracks adjuster communication and next steps.
Customer pain solved: removes confusion and stress after a crash, prevents missing or low-quality evidence, speeds up claim submission, reduces disputes and back-and-forth with insurers, and increases likelihood of fair settlements.
Core mechanics: guided checklist with time-stamped metadata, camera overlays and angle prompts, witness contact capture + voice notes, automatic scene-diagram generation from images + GPS/headings, one-click export to insurer-ready claim PDF and secure share links.
MVP path: ship a mobile web app with a prioritized 10-step capture checklist, PDF claim generator, and simple diagram templates; collect user feedback from drivers and independent adjusters to iterate.
Trust & legal design: tamper-evident metadata and hash-backed evidence, end-to-end encryption, optional police-report checklist and consent capture for witnesses, clear privacy and data-retention controls to satisfy insurers and regulators.
Monetization & partnerships: freemium (free basic capture + paid claim export), per-claim premium for legal-grade packages, subscription for commercial fleets, and B2B licensing to insurers, repair shops, rental/tow networks for lead/referral fees.
Competitive edge: standardized insurer-aligned capture checklists and instant visual diagrams that make claims easier to understand and adjudicate — turning messy accident scenes into structured, trusted evidence.
Your Roadmap:
No‑code mobile/web MVP: build a guided capture flow (step-by-step checklist + example photos) using Glide or Adalo for UI, Airtable for storage and Zapier for workflows.
Add AI claim‑drafting: connect OpenAI (or similar) via Zapier/Make to convert captured data + photos into a draft claim and a simple accident diagram (use Mermaid.js or draw.io export).
Communications & tracking: integrate Twilio for SMS updates, and a shared Airtable view as the ‘adjuster timeline’ everyone can see.
Test with 20 real users (friends/family) using scripted scenarios, iterate checklist and photo examples until capture completeness >90%.