How to build a Form Processing Agent

An AI-powered workflow that converts handwritten insurance claim forms into structured, validated, and adjuster-ready digital documents.

Challenge

Manual data entry from handwritten insurance forms is slow, error-prone, and burdensome for adjusters and intake teams.

Industry

Insurance

Department

Legal

Integrations

OpenAI

Google Docs

Workflow Overview

1. Upload Handwritten Form

  • Node: Upload Handwritten Form (doc-0)

  • What it does:
    The user uploads a scanned or photographed handwritten insurance claim form (e.g., First Report of Injury, Property Loss Notice, General Liability Incident Report).

  • How it works:
    This node uses OCR (Optical Character Recognition) to extract text from the uploaded image or PDF, making the handwritten content machine-readable for downstream processing.

2. Data Extraction

  • Node: Data Extraction (llm-0)

  • What it does:
    An AI model (LLM) reads the OCR text and extracts all key fields from the form, such as:

    • Claimant name

    • Date of loss

    • Policy number

    • Incident description

    • Contact info

    • Signatures

  • Special features:

    • Outputs the extracted data as structured JSON.

    • If a field is missing or illegible, it sets the value to null and adds a note in a flags array (e.g., "missing date", "blurry signature").

3. Validation and Formatting

  • Node: Validation and Formatting (llm-1)

  • What it does:

    • Checks the extracted fields for completeness and clarity.

    • Flags any missing or illegible data.

    • Reformats the information into a clean, readable Markdown document for insurance adjusters.

    • Adds a "flags" section at the end if any issues are found.

4. Claimant Name Extraction

  • Node: Claimant Name (llm-2)

  • What it does:

    • Specifically extracts just the claimant’s full name from the structured data produced by the Data Extraction node.

    • This is used to personalize the file name of the generated Google Doc.

5. Create Google Doc

  • Node: Create Google Doc (action-1)

  • What it does:

    • Creates a new Google Doc in your Google Drive.

    • The document’s content is the validated and formatted Markdown summary from the previous step.

    • The file is named after the claimant (e.g., "John Doe Form").

  • Connection:

    • Uses your Google Drive connection to create and store the file.

6. Output

  • Node: Output (out-0)

  • What it does:

    • Presents the final result to the user, typically showing a link to the newly created Google Doc and a summary of the processed claim.

Summary Table

Node Name

Purpose

Upload Handwritten Form

User uploads scanned/photographed form; OCR extracts text

Data Extraction

AI extracts structured fields from OCR text

Validation and Formatting

Checks completeness, flags issues, and formats for adjuster review

Claimant Name

Extracts just the claimant’s name for file naming

Create Google Doc

Creates a Google Doc with the formatted summary, named after the claimant

Output

Shows the result and Google Doc link to the user

How the Flow Works in Practice

  1. User uploads a handwritten claim form.

  2. OCR extracts the text.

  3. AI extracts all relevant fields and structures them as JSON.

  4. A second AI step validates the data, flags issues, and formats it for adjusters.

  5. The claimant’s name is extracted for use as the document title.

  6. A Google Doc is created with the formatted summary, named after the claimant.

  7. The user receives a link to the Google Doc and a summary of the processed claim.

Get started

Let’s Build AI Agents, Together

Book a demo to see how AI agents can help your team process unstructured documents and perform complex analysis faster and more accurately.

Get started

Let’s Build AI Agents, Together

Book a demo to see how AI agents can help your team process unstructured documents and perform complex analysis faster and more accurately.

Get started

Let’s Build AI Agents, Together

Book a demo to see how AI agents can help your team process unstructured documents and perform complex analysis faster and more accurately.