DeepRead Insurance Documents

Extract structured data from insurance claims, policies, EOBs, and loss reports. Pre-built schemas for claimant info, coverage, damages, adjuster notes. PII redaction for compliant sharing. 97%+ accuracy. Free 2,000 pages/month.

DeepRead.tech@uday390

Install

openclaw skills install @uday390/deepread-insurance

DeepRead Insurance Document Processing

Extract structured data from insurance claims, policy documents, Explanations of Benefits (EOBs), loss reports, and adjuster notes. Then redact claimant PII before sharing with third parties.

This skill instructs the agent to POST documents to https://api.deepread.tech and poll for results. No system files are modified.

What You Get Back

Submit a claim form and get structured JSON:

json

{
  "claim_number": {"value": "CLM-2026-078432", "hil_flag": false, "found_on_page": 1},
  "policy_number": {"value": "POL-HO3-445521", "hil_flag": false, "found_on_page": 1},
  "claimant_name": {"value": "Robert Johnson", "hil_flag": false, "found_on_page": 1},
  "date_of_loss": {"value": "2026-02-14", "hil_flag": false, "found_on_page": 1},
  "date_reported": {"value": "2026-02-15", "hil_flag": false, "found_on_page": 1},
  "loss_type": {"value": "Water Damage", "hil_flag": false, "found_on_page": 1},
  "loss_description": {"value": "Burst pipe in basement caused flooding to finished living area, damaged drywall, carpet, and personal property", "hil_flag": false, "found_on_page": 2},
  "property_address": {"value": "789 Elm Dr, Denver, CO 80202", "hil_flag": false, "found_on_page": 1},
  "coverage_type": {"value": "Homeowners HO-3", "hil_flag": false, "found_on_page": 1},
  "deductible": {"value": 1000.00, "hil_flag": false, "found_on_page": 1},
  "estimated_damages": {"value": 24500.00, "hil_flag": true, "reason": "Multiple estimates on different pages"},
  "adjuster": {"value": "Sarah Martinez, License #ADJ-44521", "hil_flag": false, "found_on_page": 3},
  "line_items": {"value": [
    {"category": "Structural", "description": "Drywall replacement — basement", "amount": 8500.00},
    {"category": "Flooring", "description": "Carpet removal and replacement", "amount": 6200.00},
    {"category": "Personal Property", "description": "Damaged furniture and electronics", "amount": 5800.00},
    {"category": "Mitigation", "description": "Water extraction and drying", "amount": 4000.00}
  ], "hil_flag": false, "found_on_page": 3},
  "status": {"value": "Under Review", "hil_flag": false, "found_on_page": 1}
}

Fields with hil_flag: true need human review. Everything else is high-confidence.

Setup

Get Your API Key

bash

open "https://www.deepread.tech/dashboard/?utm_source=clawhub"

Save it:

bash

export DEEPREAD_API_KEY="sk_live_your_key_here"

Insurance Claim Schema

Pre-built schema for insurance claims and loss reports:

json

{
  "type": "object",
  "properties": {
    "claim_number": {"type": "string", "description": "Claim number or reference ID"},
    "policy_number": {"type": "string", "description": "Insurance policy number"},
    "claimant_name": {"type": "string", "description": "Name of the claimant or insured"},
    "date_of_loss": {"type": "string", "description": "Date the loss or incident occurred (YYYY-MM-DD)"},
    "date_reported": {"type": "string", "description": "Date the claim was reported (YYYY-MM-DD)"},
    "loss_type": {"type": "string", "description": "Type of loss (Water Damage, Fire, Theft, Auto Collision, Liability, etc.)"},
    "loss_description": {"type": "string", "description": "Detailed description of the loss or incident"},
    "property_address": {"type": "string", "description": "Address of the property or location of incident"},
    "coverage_type": {"type": "string", "description": "Type of coverage (Homeowners, Auto, Commercial, Liability, etc.)"},
    "deductible": {"type": "number", "description": "Policy deductible amount"},
    "estimated_damages": {"type": "number", "description": "Total estimated damage amount"},
    "adjuster": {"type": "string", "description": "Claims adjuster name and license number"},
    "line_items": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "category": {"type": "string", "description": "Damage category (Structural, Personal Property, Medical, etc.)"},
          "description": {"type": "string", "description": "Description of damage or expense"},
          "amount": {"type": "number", "description": "Cost or estimate amount"}
        }
      },
      "description": "Itemized list of damages or expenses"
    },
    "status": {"type": "string", "description": "Claim status (Filed, Under Review, Approved, Denied, Settled)"},
    "settlement_amount": {"type": "number", "description": "Final settlement amount if resolved"},
    "denial_reason": {"type": "string", "description": "Reason for denial if applicable"}
  }
}

Extract Data From Insurance Documents

Python

python

import requests
import json
import time

API_KEY = "sk_live_YOUR_KEY"
BASE = "https://api.deepread.tech"
headers = {"X-API-Key": API_KEY}

schema = json.dumps({
    "type": "object",
    "properties": {
        "claim_number": {"type": "string", "description": "Claim number"},
        "policy_number": {"type": "string", "description": "Policy number"},
        "claimant_name": {"type": "string", "description": "Claimant name"},
        "date_of_loss": {"type": "string", "description": "Date of loss (YYYY-MM-DD)"},
        "loss_type": {"type": "string", "description": "Type of loss"},
        "loss_description": {"type": "string", "description": "Description of the loss"},
        "coverage_type": {"type": "string", "description": "Coverage type"},
        "deductible": {"type": "number", "description": "Deductible amount"},
        "estimated_damages": {"type": "number", "description": "Total estimated damages"},
        "line_items": {
            "type": "array",
            "items": {"type": "object", "properties": {
                "category": {"type": "string"},
                "description": {"type": "string"},
                "amount": {"type": "number"}
            }},
            "description": "Itemized damages"
        },
        "status": {"type": "string", "description": "Claim status"}
    }
})

with open("claim.pdf", "rb") as f:
    job = requests.post(
        f"{BASE}/v1/process",
        headers=headers,
        files={"file": f},
        data={"schema": schema},
    ).json()

job_id = job["id"]
print(f"Processing: {job_id}")

delay = 5
while True:
    time.sleep(delay)
    result = requests.get(f"{BASE}/v1/jobs/{job_id}", headers=headers).json()

    if result["status"] == "completed":
        data = result["result"]["data"]
        print(json.dumps(data, indent=2))

        for field, value in data.items():
            if isinstance(value, dict) and value.get("hil_flag"):
                print(f"\n  REVIEW: {field} — {value.get('reason')}")
        break
    elif result["status"] == "failed":
        print(f"Failed: {result.get('error')}")
        break

    delay = min(delay * 1.5, 15)

cURL

bash

curl -s -X POST https://api.deepread.tech/v1/process \
  -H "X-API-Key: $DEEPREAD_API_KEY" \
  -F "file=@claim.pdf" \
  -F 'schema={"type":"object","properties":{"claim_number":{"type":"string","description":"Claim number"},"policy_number":{"type":"string","description":"Policy number"},"claimant_name":{"type":"string","description":"Claimant name"},"date_of_loss":{"type":"string","description":"Date of loss"},"loss_type":{"type":"string","description":"Type of loss"},"estimated_damages":{"type":"number","description":"Total damages"},"status":{"type":"string","description":"Claim status"}}}'

Redact Claimant PII Before Sharing

Redact claimant personal information before sending to adjusters, contractors, or third-party reviewers:

python

# Step 1: Extract the claim data you need
with open("claim.pdf", "rb") as f:
    extract_job = requests.post(
        f"{BASE}/v1/process",
        headers=headers,
        files={"file": f},
        data={"schema": schema},
    ).json()

# Step 2: Redact PII from the original
with open("claim.pdf", "rb") as f:
    redact_job = requests.post(
        f"{BASE}/v1/pii/redact",
        headers=headers,
        files={"file": f},
    ).json()

redact_id = redact_job["id"]
delay = 5
while True:
    time.sleep(delay)
    result = requests.get(f"{BASE}/v1/pii/{redact_id}", headers=headers).json()
    if result["status"] == "completed":
        report = result["report"]
        print(f"Redacted {report['total_redactions']} PII instances")
        for pii_type, info in report["pii_detected"].items():
            print(f"  {pii_type}: {info['count']} found")
        pdf = requests.get(result["redacted_file_url"]).content
        with open("claim_redacted.pdf", "wb") as f:
            f.write(pdf)
        print("Saved: claim_redacted.pdf")
        break
    elif result["status"] == "failed":
        print(f"Failed: {result.get('error')}")
        break
    delay = min(delay * 1.5, 15)

Use Cases

Claims Processing — Extract claim numbers, dates, damage descriptions, and amounts from incoming claims
Policy Document Analysis — Pull coverage terms, limits, deductibles, and exclusions from policy documents
EOB Processing — Extract procedure codes, allowed amounts, patient responsibility from Explanations of Benefits
Loss Reports — Parse adjuster field reports for damage categories, estimates, and recommendations
Subrogation — Extract third-party liability information and recovery amounts
Fraud Detection — Batch-process claims and flag inconsistencies in dates, amounts, or descriptions
Auto Claims — Extract vehicle info, driver details, accident descriptions, and repair estimates
Workers Comp — Pull injury descriptions, medical provider info, and lost wage calculations

Tips for Insurance Documents

Specify claim-specific field names — Using "Claim number or reference ID" works better than just "number"
Include status values — Adding expected statuses (Filed, Under Review, Approved, Denied) in descriptions helps extraction
Create blueprints for recurring form types — If you process the same carrier's claim forms repeatedly, train a blueprint at deepread.tech/dashboard/optimizer for 20-30% improvement
Always redact before external sharing — Use PII redaction before sending to contractors, adjusters, or legal teams

BYOK — Zero Processing Costs

Connect your own OpenAI, Google, or OpenRouter key via the dashboard. All document processing routes through your provider — zero DeepRead LLM costs, page quota skipped.

Set it up: https://www.deepread.tech/dashboard/byok

Related DeepRead Skills

deepread-ocr — General OCR and structured extraction — clawhub install uday390/deepread-ocr
deepread-pii — Redact PII from any document — clawhub install uday390/deepread-pii
deepread-form-fill — Fill PDF forms with AI vision — clawhub install uday390/deepread-form-fill
deepread-invoice — Invoice and receipt processing — clawhub install uday390/deepread-invoice
deepread-medical — Medical records processing — clawhub install uday390/deepread-medical
deepread-legal — Legal document processing — clawhub install uday390/deepread-legal
deepread-agent-setup — OAuth device flow authentication — clawhub install uday390/deepread-agent-setup
deepread-byok — Bring Your Own Key setup — clawhub install uday390/deepread-byok

Support

Dashboard: https://www.deepread.tech/dashboard
Demo Repo: https://github.com/deepread-tech/deepread-demo
n8n Node: https://www.npmjs.com/package/n8n-nodes-deepread
Issues: https://github.com/deepread-tech/deep-read-service/issues
Email: hello@deepread.tech

Get started free: https://www.deepread.tech/dashboard/?utm_source=clawhub

DeepRead Insurance Documents

Install

DeepRead Insurance Document Processing

What You Get Back

Setup

Get Your API Key

Insurance Claim Schema

Extract Data From Insurance Documents

Python

cURL

Redact Claimant PII Before Sharing

Use Cases

Tips for Insurance Documents

BYOK — Zero Processing Costs

Related DeepRead Skills

Support

Related skills