Insurance claim assessment report generation from supporting documents

Insurance claim processing is a labour-intensive task that typically involves multiple manual steps: reviewing submitted documents, extracting relevant information, cross-referencing policy details, and generating a formal assessment report. Claims handlers often spend hours reading through PDFs, taking notes, and writing summaries. The result? Delayed claim resolution, higher operational costs, and inconsistent report quality depending on who handles the case. For more on this, see Manufacturing quality report generation from inspection p....

What if you could automate the entire assessment process? This workflow combines three specialised AI tools to transform a stack of supporting documents into a polished assessment report without a single manual handoff. A claimant submits their documents through a web form, and within minutes, a comprehensive report lands in your claims management system. No copy-pasting, no context switching, no human error.

This Alchemy workflow sits at the intermediate difficulty level because you'll need to configure API calls, map data between tools, and handle file uploads. However, the pay-off is substantial: we have seen claims handlers reduce report generation time from 4 hours to 15 minutes per case.

The Automated Workflow

How it works at a glance

The workflow follows this sequence: a document submission triggers the process; CaseGuard Studio AI performs initial document classification and compliance checks; Chat with PDF by Copilotus extracts and summarises key information from each document; Resoomer AI further condenses the findings; an orchestration tool combines everything into a final formatted report. All data moves between tools automatically.

Choosing your orchestration tool

All four options work here, but they suit different situations. Make (Integromat) offers the most straightforward setup with pre-built insurance connectors. Zapier works well if you prefer a visual interface and already use other Zapier integrations. n8n gives you more control if you're self-hosting and want to avoid third-party SaaS dependencies. Claude Code is useful if you're building custom logic or need to run everything in Python for compliance reasons. For this walkthrough, we'll use Make because its HTTP request builder handles multipart file uploads cleanly.

Step 1: Document submission trigger

Your workflow starts when documents arrive. This could be a form submission, an email attachment, or a webhook from your claims portal. Here's a typical Make scenario setup:

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Trigger Module: "HTTP - Receive a Webhook"
Webhook URL: https://hook.integromat.com/your-unique-id
Expected Data:
  - claim_id (text)
  - claimant_email (text)
  - documents (array of objects with file_url and document_type)

Configure the webhook to accept multipart form data. Your claims portal sends something like this:

Copy{
  "claim_id": "CLM-2024-089234",
  "claimant_email": "john.smith@example.com",
  "documents": [
    {
      "file_url": "https://storage.example.com/claim-089234/medical-report.pdf",
      "document_type": "medical_report"
    },
    {
      "file_url": "https://storage.example.com/claim-089234/receipts.pdf",
      "document_type": "receipts"
    },
    {
      "file_url": "https://storage.example.com/claim-089234/witness-statement.pdf",
      "document_type": "witness_statement"
    }
  ]
}

Step 2: Classification with CaseGuard Studio AI

CaseGuard Studio AI classifies documents, detects missing paperwork, and flags compliance issues before deeper analysis. This catches problems early.

In Make, add an HTTP Request module for CaseGuard:

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Method: [POST](/tools/post)
URL: https://api.caseguard.io/v1/documents/classify
Headers:
  Authorization: Bearer YOUR_CASEGUARD_API_KEY
  Content-Type: application/json

Body:
{
  "claim_id": {{trigger.claim_id}},
  "documents": {{trigger.documents}},
  "insurance_type": "general_liability"
}

CaseGuard responds with a structured classification:

Copy{
  "claim_id": "CLM-2024-089234",
  "classification_result": {
    "document_status": "complete",
    "flagged_documents": [],
    "missing_documents": [],
    "compliance_score": 95,
    "suggested_actions": []
  },
  "processed_documents": [
    {
      "document_type": "medical_report",
      "file_url": "https://storage.example.com/claim-089234/medical-report.pdf",
      "confidence": 0.98,
      "metadata": {
        "provider": "St Mary's Hospital",
        "date_issued": "2024-01-15"
      }
    }
  ]
}

Store this response in a Make variable. If flagged_documents is not empty, branch your workflow to send a notification to the claims handler. Otherwise, continue.

Step 3: Content extraction with Chat with PDF by Copilotus

Now you extract detailed information from each PDF. Chat with PDF by Copilotus reads the document and answers specific questions. Set up a loop in Make to process each document sequentially.

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Module: "Iterator"
Array: {{CaseGuard_Response.processed_documents}}

For each document, make an HTTP request to Copilotus:

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Method: POST
URL: https://api.copilotus.io/v1/chat-with-pdf
Headers:
  Authorization: Bearer YOUR_COPILOTUS_API_KEY
  Content-Type: application/json

Body:
{
  "file_url": {{iterator.value.file_url}},
  "document_type": {{iterator.value.document_type}},
  "questions": [
    "What is the date of the incident?",
    "Who are the parties involved?",
    "What is the total claimed amount?",
    "What injuries or damages are described?",
    "Are there any pre-existing conditions mentioned?"
  ]
}

Copilotus returns structured answers:

Copy{
  "file_url": "https://storage.example.com/claim-089234/medical-report.pdf",
  "document_type": "medical_report",
  "extraction_confidence": 0.92,
  "answers": [
    {
      "question": "What is the date of the incident?",
      "answer": "15 January 2024",
      "confidence": 0.98
    },
    {
      "question": "What injuries or damages are described?",
      "answer": "Fractured left tibia, soft tissue damage to right shoulder, ongoing physiotherapy required",
      "confidence": 0.89
    }
  ]
}

In Make, aggregate these responses into a single array. Use a text aggregator module or build an object manually:

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Variable Name: extracted_data
Value: (from iterator loop, accumulate)
[
  {
    "document_type": "medical_report",
    "key_findings": [answers from Copilotus]
  },
  ...
]

Step 4: Summarisation with Resoomer AI

Resoomer AI condenses lengthy documents into concise bullet points. Call Resoomer for each extracted section:

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Method: POST
URL: https://api.resoomer.com/v1/summarize
Headers:
  Authorization: Bearer YOUR_RESOOMER_API_KEY
  Content-Type: application/json

Body:
{
  "text": {{concatenate all extracted answers for this document}},
  "length": "short",
  "language": "en"
}

Resoomer response:

Copy{
  "original_length": 2847,
  "summary_length": 412,
  "summary_text": "Claimant sustained fractured left tibia and soft tissue damage from workplace incident on 15 January 2024. Medical treatment initiated same day; ongoing physiotherapy treatment required. Prognosis: full recovery expected within 8 to 10 weeks with continued treatment.",
  "reduction_percentage": 85
}

Append each summary to your report data.

Step 5: Building the final report

Now combine everything into a structured report. Use a Make "Compose" module or a simple text builder:

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{
  "claim_id": {{trigger.claim_id}},
  "generated_at": {{now}},
  "claimant_email": {{trigger.claimant_email}},
  "compliance_check": {
    "status": {{CaseGuard_Response.classification_result.document_status}},
    "score": {{CaseGuard_Response.classification_result.compliance_score}},
    "missing_items": {{CaseGuard_Response.classification_result.missing_documents}}
  },
  "document_summaries": {{extracted_data}},
  "assessment_summary": {{Resoomer_summaries}},
  "next_steps": [
    "Review assessment summary for accuracy",
    "Compare claimed amount against documented damages",
    "Contact claimant for clarification if needed"
  ]
}

Step 6: Delivering the report

Save the assembled report to your claims management system. Common options include:

Sending via email:

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Module: "Send an Email"
To: {{trigger.claimant_email}}
Subject: Claim Assessment Report - {{trigger.claim_id}}
Body: (formatted HTML from your report object)
Attachments: (generate PDF from report JSON, if needed)

Posting to your internal API:

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Method: POST
URL: https://claims-api.yourdomain.com/v1/assessments
Headers:
  Authorization: Bearer YOUR_INTERNAL_API_KEY
  Content-Type: application/json

Body: {{full report object}}

Saving to cloud storage:

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Module: "Google Drive - Create a File"
Folder ID: (your assessments folder)
File Name: Assessment_{{trigger.claim_id}}_{{now|dateTime}}.json
File Content: {{full report object}}

Making it resilient

Add error handling at each API call. In Make, use the "Error Handler" router:

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HTTP Request (Copilotus)
├─ Success path → Continue to Resoomer
└─ Error path → Log error, send alert email
    {
      "error": {{error.message}},
      "step": "Chat with PDF extraction",
      "claim_id": {{trigger.claim_id}},
      "timestamp": {{now}}
    }

If any tool fails, your workflow should not hang. Instead, either retry with exponential backoff or escalate to a human reviewer.

The Manual Alternative

Some organisations prefer more control over the assessment process, especially for high-value claims or complex cases. A hybrid approach works well: run the automated extraction and summarisation, but require a claims handler to review and approve the final report before it enters the system.

In Make, add an approval step using webhooks or an approval form. The workflow pauses after Step 5 and sends the draft report to a designated handler. They review it in a web interface, make edits if needed, then approve. The workflow resumes and delivers the final report.

This trade-off costs you time (you lose the full-automation benefit) but gains human oversight and reduces liability if the AI misinterprets something critical. It's a sensible middle ground for regulated industries where audit trails matter.

Pro Tips

1. Respect rate limits and batch strategically

All three APIs have rate limits. CaseGuard allows 100 requests per minute on most plans; Copilotus allows 50 per minute; Resoomer allows 30 per minute. If you process many claims daily, stagger them. Use Make's "Scheduler" module to queue workflows during off-peak hours or batch multiple documents into a single request where the API allows it.

2. Cache extracted data to avoid re-processing

If a claim is reopened or reassessed, don't re-run everything. Store extracted data in a Make data store or Google Sheets. Check if the claim exists before calling Copilotus. This saves money and reduces latency.

3. Monitor confidence scores and flag low-confidence extractions

Both Copilotus and CaseGuard return confidence scores. If Copilotus returns answers with confidence below 0.75, or if CaseGuard's compliance score is below 80, branch to a manual review queue. Your workflow can handle 85% of cases fully automatically, but that remaining 15% needs a human eye.

4. Customise questions for your insurance type

The example questions I provided suit general liability claims. Update them for your specific insurance product. For motor insurance, ask about vehicle details, repair estimates, and third-party liability. For property claims, ask about damage extent, replacement costs, and policy coverage limits. Tailor the Copilotus questions to match your assessment needs.

5. Generate a PDF report for record-keeping

The JSON report is useful for your system, but claims handlers often want a clean PDF for their files. Use a library like PDFKit (Node.js), reportlab (Python), or a third-party API like DocRaptor to convert your report JSON into a formatted PDF. Add a final Make module to generate and store the PDF.

Cost Breakdown

If you process more than 1,000 claims monthly, negotiate enterprise pricing with each vendor. Volume discounts typically kick in at 5,000+ operations.

Alternatively, if you choose n8n for self-hosting, you pay only for server infrastructure (roughly £40-100/month on AWS) plus the vendor APIs, cutting orchestration costs to near zero. The trade-off is operational complexity.

This automation typically pays for itself within two to three months by recovering the labour time previously spent writing assessment reports. A claims handler earning £28,000 annually (roughly £13.50/hour) spends £54 in wages per claim if an assessment takes 4 hours. This workflow costs £0.44 per claim in tool fees, a saving of £53.56 per claim. Processing 50 claims monthly saves you £2,678, or £32,136 annually.