Freight claims automation that cuts cycle time without the chase.
Create complete cargo-loss and damage claim files, chase missing evidence, and draft correspondence while an examiner decides liability and settlement.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Claims stall because photos, invoices, PODs, inspection records, and notice deadlines are scattered; staff repeatedly request the same missing items.
Claims examiners, customer service, warehouse and carrier relations teams
Trigger to act: Claims cycle time is high, write-offs are increasing, customers lack status visibility, or a 3PL needs standardized claim quality across branches.
The result you can model before you sign.
Illustrative only: 400 claims/month × 20 minutes removed × $39 loaded hourly cost ÷ 60 = $5,200 monthly capacity, excluding recoveries or faster customer credits.
The outcome, plainly: Create complete cargo-loss and damage claim files, chase missing evidence, and draft correspondence while an examiner decides liability and settlement.
Inputs in. A cited, review-ready result out. Your expert decides.
A Document AI + grounded RAG copilot. Every material fact is grounded in an allowed source and returned with its identifier, no invented data.
Claude Sonnet 4.6 or GPT-5.6 Terra for evidence-aware extraction and reasoning; Gemini 2.5 Flash-Lite or Claude Haiku 4.5 for high-volume classification and normalization. Amazon Textract or Azure AI Document Intelligence; PostgreSQL + pgvector; Pinecone only when scale/latency requires it.
- Claim form
- BOL/POD
- Photos
- Commercial invoice
- Inspection/salvage evidence
- Correspondence
AI-Native, not autonomous. Judgment stays with your people.
The machine does the work; the human’s role narrows to the one thing that matters, judgment. That constraint is what makes it safe to deploy.
A claims examiner decides liability, reserve, settlement, denial, and legal escalation; image analysis is treated as evidence assistance, not proof.
A scorecard, not a demo. We baseline what breaks in production.
Every deployment ships with an evaluation suite. These are the numbers we baseline before launch and monitor after.
The category is crowded. Most of it isn’t built for your workflow.
Fixed-scope, tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in from day one. A price you own, not a subscription you rent.
It plugs into the stack you already run.
No rip-and-replace. Access is scoped to the minimum data necessary, isolated per tenant, and fully logged.
Transparent by design. The build price buys the workflow and the proof.
A fixed implementation fee plus a monthly bill that scales with volume and governance. No hidden seats.
- ✓ One process / scope
- ✓ Live workflow on your data
- ✓ Baseline evaluation suite
- ✓ Measured vs. current process
- ✓ Full scope & integration
- ✓ Human-review UI & audit trail
- ✓ Write-back to your systems
- ✓ Production evals & monitoring
- ✓ Multi-facility rollout
- ✓ Advanced security & compliance
- ✓ Custom control & escalation
- ✓ Dedicated evaluation program
8,000 to 30,000 pages per month plus retrieval, generation, vector search and evidence storage.
Planning assumptions, not vendor quotations. Your Claims system and other platform licenses are separate and owned by you. Figures confirmed during scoping.
“His vast knowledge of technologies and a natural problem-solving mindset consistently lead us through complex challenges with clarity and confidence.”
Questions serious buyers ask.
Does the AI act on its own?
No. A claims examiner decides liability, reserve, settlement, denial, and legal escalation; image analysis is treated as evidence assistance, not proof. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: no autonomous liability or settlement decision; no legal advice; no alteration of evidence; no guarantee of carrier recovery.
How do you stop it inventing facts?
Every material claim is grounded in an allowed source record and returned with its source identifier. The system separates observed facts, model inference, and missing information, and routes to a human whenever confidence is low, evidence conflicts, or an adverse outcome is possible.
What does it cost to run each month?
A usage bill of roughly $1,000–6,200 per month (8,000 to 30,000 pages per month plus retrieval, generation, vector search and evidence storage), plus a $2,000 per month managed retainer for evaluation, monitoring and maintenance. Your existing platform licenses are separate and already yours. Exact figures are confirmed during scoping.
Do we need a ChatGPT or Claude subscription?
No consumer ChatGPT or Claude subscription is required for the production workflow. The client needs an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from CargoNet?
Tools like CargoNet, Sedgwick, and MercuryGate claims modules are broad platforms you adapt to. This is a fixed-scope implementation tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in, and a transparent price instead of a seat subscription.
How long until it is live, and how do we prove it works?
We baseline “Complete-on-first-review” first, then measure against that baseline. You see the scorecard before expanding scope: the evaluation suite ships with the system, not as an afterthought.
Bring your real numbers. Leave with a fixed-scope plan.
A 30-minute engineering-led working session, no slideware. You leave with a sized opportunity estimate, a fixed-scope pilot plan, and the integration & human-review path mapped.
VP of Growth at ViitorCloud · senior delivery owner confirmed before paid work