Vehicle Insurance Claim Approvals
Monitors for vehicle insurance claim emails, extracts PDF data via OCR, evaluates against underwriting rules.
ops and IT teams
What it does
You get instant email approvals for vehicle insurance claims as soon as your agent finds the PDF attachments in Gmail. It automatically checks for duplicates, extracts data via OCR, evaluates the case against underwriting rules, and sends you signed approval or rejection letters directly to your inbox.
Step by step
- Set up a Gmail trigger to watch for emails with specific subject lines or sender domains.
- Configure Google Sheets as a lookup table for existing claim IDs to prevent duplicate processing.
- Use the PDF API Hub OCR endpoint to convert attached PDFs into structured JSON data.
Where the LLM does the work
- Prompt the OpenAI model with extracted claim details and underwriting rules to determine approval status.
- Generate personalized rejection letters that explain specific policy violations in natural language.
- Refine OCR output by having the LLM correct ambiguous character recognition errors in medical or vehicle codes.
Watch out for
OCR accuracy drops on low-resolution scans so implement a validation step to reject blurry images before processing.
Email attachments may exceed Gmail size limits for direct parsing, so use temporary storage links if files are large.
Tools that fit
PDF API Hub API Extract text from PDFs and generate signed letters
OpenAI Chat Model LLM Evaluate claims and determine approval status
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Vehicle Insurance Claim Approvals: Monitors for vehicle insurance claim emails, extracts PDF data via OCR, evaluates against underwriting rules. ## Specification - What it does: You get instant email approvals for vehicle insurance claims as soon as your agent finds the PDF attachments in Gmail. It automatically checks for duplicates, extracts data via OCR, evaluates the case against underwriting rules, and sends you signed approval or rejection letters directly to your inbox. - Trigger: Triggered by an event (Event · on new email with PDF attachment) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - Gmail: Monitor claims inbox and send decision emails - Google Sheets: Log claims and detect duplicates - PDF API Hub: Extract text from PDFs and generate signed letters - OpenAI Chat Model: Evaluate claims and determine approval status ## Known pitfalls, handle each one explicitly in your implementation 1. OCR accuracy drops on low-resolution scans so implement a validation step to reject blurry images before processing. 2. Email attachments may exceed Gmail size limits for direct parsing, so use temporary storage links if files are large. ## Reference implementation https://n8n.io/workflows/15232 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/automate-vehicle-insurance-claim-approvals/ via usecasesforagents.com
Frequently asked questions
Can I use a different Email than Gmail?
Yes. Gmail is only the example email in this recipe. The same flow works with Brevo, KlickTipp, Lemlist and Mailchimp. Swap the email connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for Ops-IT teams who need one more recurring task off a human's plate.