Warranty-Claim Drafter | Real Minds AI
AutomotiveDraftinglive

Warranty-Claim Drafter

Reads the repair order and technician notes, drafts the OEM warranty claim with failure, defect and labour codes, and flags missing information — for a warranty clerk to check and submit.

realmindsai.com.au/theater/demos/automotive_warranty-claim-drafter.html · sandbox · read-only
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How it would work

One agent, grounded in your library, with citations on every paragraph.

01 · input
Member question
02 · agent
Your agent
03 · output
Cited answer or escalation
The problem it solves

Warranty claims are coded by hand from unstructured technician notes through OEM-specific portals, at roughly 8–12 minutes a claim across multiple brands. The drudgery isn't the judgement — it's translating notes into the right codes and re-keying the same fields, which steals skilled back-office time and delays payment.

This pattern extracts the claim detail from the repair order and notes, drafts the claim with the failure, defect, symptom and labour codes tied back to the line that supports each one, and flags anything missing or ambiguous before submission. A warranty clerk reviews and submits — the tool never files a claim itself.

TA
Tracy Anthony · Co-Founder & CEO · wrote up this design
What it would take to build

Estimated build: 3–5 weeks. Most of it is template work we've already done.

Estimated build time
3–5weeks
Diagnostic · build · soft launch · review.
Reused from template
~70%
Agent shell · retrieval · audit · deployment.
Bespoke to this skin
~30%
OEM code-set encoding, RO/notes source mapping.
stack · Power Platform · document AI · review UI
What it would cost for your org

Fixed scope, fixed price, fixed dates.

The cost band reflects the engagement shape, not a per-feature line item. We work on fixed scope, fixed price, fixed dates — see the services catalogue for what falls inside each band.

Engagement band
A bite-sized first piece → pilot build → embedded support. Start small, scale on proof — most builds land in the pilot band.

Considering this for your org?

The honest place to start is a bite-sized first piece — one contained change, low risk. Tell us where it hurts; we’ll play it back, scope it, and show you what’s possible.

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