AI Document Processing/Extraction with Unstract

Introduction

In today’s digital era, AI document processing, and AI document extraction have become indispensable for modern enterprises. By leveraging artificial intelligence to automate the transformation of unstructured data, businesses now accelerate workflows and boost accuracy. Unlike conventional methods, document processing AI techniques extract, classify, and structure content from PDFs, scanned images, and handwritten forms—ushering in a new age of AI-based document processing.

Traditional OCR vs. AI-Powered Document Processing

Conventional OCR methods offer basic text conversion; however, they often lack the sophistication required for comprehensive document extraction. Traditional approaches fall short when facing complex layouts, handwritten text, or multi-column documents. In contrast, document extraction ai solutions using document processing with ai deliver enhanced context awareness by:

• Understanding Structure: They identify tables, forms, and multi-column text.
• Extracting Key Entities: The systems reliably capture names, dates, numbers, and other vital information.
• Handling Variations: They manage diverse formatting, quality, and language challenges—surpassing the limitations of basic OCR.

This leap forward in AI document processing helps companies save time and reduce the risk of errors compared to manual data entry.

Real-World Applications of AI Document Processing

Industries around the globe have adopted AI document extraction and AI document processing to streamline their workflows. Some key sectors include:

• Finance & Banking: Automating extraction from invoices, bank statements, and tax forms with advanced document extraction techniques.
• Healthcare: Digitizing patient records, prescriptions, and insurance claims using document processing AI.
• Legal & Compliance: Extracting clauses from contracts and agreements via document extraction AI solutions.
• Logistics & Supply Chain: Managing shipping documents and purchase orders efficiently through AI-based document processing methods.

By reducing manual intervention, these techniques improve overall operational efficiency.

Challenges with Traditional Methods

Manual Processing Obstacles

Many organizations still rely on manual document handling, a process that is:

• Time-Consuming: Hours are lost when employees manually input data.
• Error-Prone: Human error often leads to inaccuracies in document extraction.
• Costly: High labor costs and inefficiencies hinder business growth.

Limitations of Standard OCR

While traditional OCR can capture text, it struggles with:

• Complex Layouts: Multi-column documents and tables lose their formatting.
• Handwritten Inputs: OCR fails to accurately capture handwritten text and checkboxes.
• Lack of Context: Plain text extraction misses the deeper meaning and structure that ai document extraction methods provide.

Without document processing with AI, companies face delays and increased compliance risks.

The Advent of AI in Document Processing

Enhancing Extraction Accuracy

Ai document processing leverages machine learning and natural language processing (NLP) to overcome the shortcomings of basic OCR. The advanced techniques include:

• Higher Accuracy: AI models analyze document structure to produce clean, structured data.
• Contextual Understanding: The extraction process recognizes key entities such as names, dates, and financial figures, making document extraction more robust.
• Versatility: Whether dealing with handwritten content or complex layouts, document processing ai adapts to varied data sources.

These innovations in ai document processing enable businesses to process documents in minutes rather than hours.

Essential Benefits for Modern Businesses

Implementing AI document extraction and AI document processing offers several tangible advantages:

• Speed: Automated extraction reduces turnaround time dramatically.
• Cost Efficiency: Lower operational costs due to reduced manual intervention.
• Enhanced Accuracy & Compliance: Reliable data extraction minimizes errors, a critical factor in sectors like finance and healthcare.

Overall, AI-based document processing is transforming industries by digitizing raw information into actionable insights.

Introducing Unstract: AI-Powered Document Processing

Unstract stands out as an advanced, open-source platform that delivers exceptional document extraction capabilities. It automates the conversion of complex documents into structured, machine-readable data by integrating modern AI document processing techniques.

Unstract is an open-source no-code LLM platform to launch APIs and ETL pipelines to structure unstructured documents. Get started with this quick guide.

Key Features of Unstract

• AI-Powered Extraction: Utilizes NLP, embeddings, and large language models to extract meaningful data, ensuring superior document processing AI.
• Versatile Format Support: Processes PDFs, scanned images, forms, and even handwritten text to guarantee precise AI document extraction.
• Scalable ETL Workflows: Seamlessly integrates with cloud data platforms such as Snowflake, making AI document processing scalable.
• Customizable Prompt-Based Extraction: With Prompt Studio, users define specific fields—like names, dates, and financial terms—to be captured during document extraction.
• Automated Execution: Designed to process large volumes of documents, Unstract significantly cuts down the manual effort required in AI-based document processing.

Important Reminder: LLMWhisperer, a crucial component of Unstract, functions strictly as an OCR-based text extractor and is not powered by large language models. This distinction is key: while Unstract performs ai document processing with high intelligence, LLMWhisperer focuses on preserving the document’s layout and raw text for further analysis.

The Role of LLMWhisperer in the Unstract Workflow

LLMWhisperer plays a critical role in the Unstract workflow by enhancing the document extraction process through high-accuracy OCR-based parsing. It ensures that documents are accurately converted into structured text, making them suitable for AI-powered analysis and automation.

How LLMWhisperer Enhances Extraction:

• Extracting Raw Text: Converts scanned PDFs and images into structured text, enabling high-fidelity document extraction for further processing.
• Preserving Layout: Maintains tables, checkboxes, multi-column formatting, and structured data, ensuring no formatting loss.
• Handling Handwritten Content: Recognizes and extracts handwritten inputs, even from complex or faded documents, ensuring no critical data is missed.
• Serving as a Pre-Processing Step: LLMWhisperer feeds accurately parsed text into Unstract’s AI-powered document extraction engine, allowing for deeper analysis and intelligent insights.

Showcasing LLMWhisperer in Action: Playground Demonstration

To see LLMWhisperer’s capabilities in action, you can use the LLMWhisperer Playground:

1. Go to the Playground: LLMWhisperer Playground
2. Upload a Bank Account Statement: Choose a bank statement PDF containing structured tables, transactions, and financial details.
3. Observe the Magic: Within seconds, you will see that each and every detail is extracted with near-perfect accuracy. The layout, table structures, and formatting are fully preserved, ensuring the extracted text is as structured as the original document.

This demonstration highlights why LLMWhisperer is a game-changer—bridging the gap between traditional OCR and AI-powered document processing, ensuring error-free data extraction and superior layout retention.

Why Unstract + LLMWhisperer is the Ultimate Solution

Combining the robust capabilities of Unstract with LLMWhisperer’s OCR-based extraction delivers a comprehensive solution for document processing ai challenges. Together, they allow organizations to:

• Automate the entire document lifecycle—from raw data capture to actionable insights.
• Maintain the structural integrity of documents during ai document extraction.
• Integrate seamlessly with platforms like Snowflake for advanced data warehousing and analytics.

This blend of technologies positions businesses to make data-driven decisions faster and more efficiently than ever before.

Unstract 101: Leveraging AI to Convert Unstructured Documents into Usable Data

Watch this webinar/demo to explore Unstract, a platform for LLM-powered unstructured data extraction. Learn how to process complex documents—like those with images, forms, and multi-layout tables—without the need for pre-training.

Setting Up Unstract for AI-Powered Data Extraction

Extracting structured data from bank account statements requires a combination of AI-powered tools, including OCR, embeddings, vector databases, and prompt-based extraction techniques. In this section, we will set up Unstract to process bank statements and extract key financial details such as:

• Account Holder’s Name
• Bank Name & Branch Details
• Account Number & Type
• Transaction Details (Date, Description, Amount, Balance)
• Important Dates (Statement Date, Due Date, Last Payment Date)
• Credit & Debit Information
• Card Details (if applicable) – Credit Card Type, Credit Limit, Minimum Payment

With Unstract, this manual data extraction can be completely automated, ensuring accuracy, speed, and efficiency in financial data processing.

Setting Up Key Components in Unstract

1. Getting Started with Unstract

• Sign Up on Unstract: Visit the Unstract website and create an account.

2. Configuring Core Components

Unstract’s modular setup allows users to integrate different AI-powered services to enhance data extraction. If additional resources are needed, they can be configured in the SETTINGS menu.

Adding an LLM (e.g., OpenAI) for Advanced Text Processing

• Navigate to SETTINGS → LLMs
• Click + New LLM Profile
• Select OpenAI (or another LLM provider)
• Enter the API key and finalize the setup

Why LLMs? LLMs enhance context understanding and help extract meaningful insights from complex text, making them vital for bank statement processing.

Adding an Embedding Provider for Contextual Data Processing

• Go to SETTINGS → Embedding
• Click + New Embedding Profile
• Select an embedding provider and follow the setup instructions

Why Embeddings? They convert raw textual data into semantic vectors, allowing Unstract to understand relationships between financial terms and improve information retrieval.

Connecting a Vector Database for Efficient Data Storage

• Navigate to SETTINGS → Vector DBs
• Click + New Vector DB Profile
• Choose a vector database type (e.g., PostgreSQL, Pinecone) and complete the setup

Why Vector DBs? They store structured and unstructured financial data for fast retrieval, ensuring seamless processing of past statements and transactions.

Integrating LLMWhisperer for OCR-Based Data Extraction

• Go to SETTINGS → Text Extractor
• Click + New Text Extractor
• Select LLMWhisperer as the extraction tool

Why LLMWhisperer?

• Extracts text from scanned or digital bank statements
• Preserves tables, columns, and formatting
• Ensures high-fidelity financial data extraction

We will use below bank account statement for this:

3. Setting Up a Prompt Studio Project

Prompt Studio in Unstract allows users to define customized AI prompts that extract specific details from bank statements.

Creating the Bank Statement Extraction Project

• Navigate to Prompt Studio
• Click New Project and name it “Bank Statement Parser”
• Upload bank statement PDFs under Manage Documents
• Define custom prompts for extracting critical information

4. Writing Effective Prompts for Bank Statement Data Extraction

Prompts must be carefully crafted to extract key financial details accurately. Below are some optimized prompts for bank statements:

Extracting Personal & Account Information

• “Identify the account holder’s name, address, and contact number from the statement.”
• “Extract the bank name, branch details, and account number from the document.”

Extracting Transaction Details

• “List all debit and credit transactions, including date, description, and amount, preserving the table format.”
• “Extract the current balance and previous statement balance from the bank statement.”

Extracting Key Dates & Limits

• “Identify and extract statement date, due date, and last payment date with correct labels.”
• “Extract credit card type, credit limit, and minimum payment amount (if available).”

By setting the output format to JSON, the extracted financial details remain structured and machine-readable, allowing easy integration with databases like Snowflake.

5. Running the Prompts & Viewing the Extracted Data

After defining the prompts:

• Click “Run All Prompts” for the uploaded bank statement
• The AI extracts structured JSON output, including:

{
  "customer_details": {
    "address": "894D Beachview St, STE 108",
    "city": "Orlando",
    "name": "Particia Wilson",
    "state": "FL",
    "zip": "32256"
  },
  "branch_details": {
    "name": "Olando"
  },
  "card_details": {
    "card_number": "2547-2333-2541-2345"
  },
  "total_due_amount": {
    "value": "1,249.95"
  },
  "transactions": {
    "transactions": [
      {
        "amount": 19.95,
        "date": "03/01/23",
        "description": "Account Interest"
      },
      {
        "amount": 20000,
        "date": "04/01/23",
        "description": "Deposit Inward"
      },
      {
        "amount": 600,
        "date": "05/01/23",
        "description": "Withdrawal"
      },
      {
        "amount": 630,
        "date": "06/01/23",
        "description": "ATM Cash IL02"
      },
      {
        "amount": 1430,
        "date": "07/01/23",
        "description": "British Airways ticket"
      },
      {
        "amount": 100,
        "date": "08/01/23",
        "description": "Cost of Bank transactions"
      },
      {
        "amount": 500,
        "date": "09/01/23",
        "description": "Branch Deposit"
      },
      {
        "amount": 1.05,
        "date": "10/01/23",
        "description": "Bank Account Debits Tax"
      },
      {
        "amount": 1.67,
        "date": "11/01/23",
        "description": "VIC FID Charge"
      }
    ]
  },
  "bank_details": {
    "Bank": "ACBNI",
    "Bank Address": "231 Valley Farms Street, Santa Monica, CA",
    "Bank Email": "bnibank@domain.com",
    "Branch Name": "Olando"
  },
  "balance_amout": {
    "value": "31,678.69"
  },
  "total_debit": {
    "value": "1,249.95"
  },
  "total_credit": {
    "value": "22,032.72"
  },
  "account_details": {
    "account_number": "45784367890",
    "account_type": "Personal Current Account"
  }
}

This extracted data is accurate, structured, and ready for integration into Snowflake or any other database system.

Introduction to Snowflake

Snowflake is a modern cloud-based data warehousing platform that perfectly complements ai document processing systems. Designed to store, process, and analyze both structured and semi-structured data, Snowflake provides:

• Scalability & Performance: Automatically scales computing resources based on demand, which is vital when processing large volumes of document extraction data.
• Separation of Storage & Compute: Offers flexibility and cost optimization, essential for integrating with ai based document processing systems.
• Multi-Cloud Compatibility: Supports deployment across AWS, Azure, and Google Cloud, ensuring that ai document processing can be implemented in any environment.
• Support for Semi-Structured Data: Natively handles JSON, Avro, Parquet, and XML formats, allowing seamless integration of data captured through document extraction ai methods.
• Secure Data Sharing: Facilitates the secure sharing of extracted data across organizations, making document processing with ai a viable solution for modern enterprises.

Integrating Unstract with Snowflake empowers businesses to store and analyze data extracted via ai document extraction more effectively, driving actionable insights and improved decision-making.

Connect Source (Dropbox) and Destination (Snowflake)

To automate the extraction of bank statements from Dropbox and store the structured financial data in Snowflake, we need to create a workflow in Unstract.

Before setting up the workflow, we must first convert our Prompt Studio project into a tool so it can be used in the workflow.

Step 1: Export the Project as a Tool

1. Go to Prompt Studio and open the Bank Statement Extraction project.
2. Click the ‘Export as Tool’ icon (top right corner).
3. The project will now be available as a reusable tool in the workflow builder.

Step 2: Create a New Workflow

1. Navigate to BUILD → Workflows and click on ‘+ New Workflow’.
2. Name the workflow “Bank Statement Processing”.
3. In the Tools section (right-hand side), find the tool created from your project (e.g., Bank Statement Extractor).

Drag and drop the tool into the workflow editor on the left-hand side.

Step 3: Connect Dropbox as the Input Source

Since the bank statements are stored in Dropbox, we need to configure Dropbox as the document source.

1. In the input section, select ‘File System’.
2. Click the gear icon to configure additional settings.
3. Select Dropbox as the file system.
4. Add the ‘Access token’ for the dropbox.
5. Select the ‘Unstract’s App’ folder where the 4 bank statement PDFs are stored.
6. Choose PDF documents as the file type to process.

For detailed documentation on Dropbox integration, refer to:
📌 Unstract Dropbox Integration Guide

Step 4: Configure Snowflake as the Output Destination

Now that Dropbox is connected as the input source, we need to configure Snowflake as the destination for structured data.

1. In the output section, select ‘Database’.
2. Click the gear icon to configure Snowflake access.
3. Enter the Snowflake connection details, including:
   • Account Identifier
   • Warehouse
   • Database Name
   • Schema
   • Table Name
   • User Credentials
4. Ensure that Snowflake is set up correctly to receive structured JSON data.
5. Enter the database details like column name, table name, etc.

For more details on setting up Snowflake with Unstract, refer to the official documentation.

Execute the Workflow

With Dropbox as the source and Snowflake as the destination, the workflow is now fully configured.

Running the Workflow

1. Click the ‘Run Workflow’ icon to start processing the bank statements.
2. The workflow will automatically process all PDF files located in the configured Dropbox folder (‘Unstract’s App’).
3. In this example, 4 bank statements are being processed.

Verifying Extracted Data in Snowflake

Once the workflow completes processing, we can check the extracted data in Snowflake by executing an SQL query.

Example Query to View Extracted Bank Statement Data:

SELECT*FROM BANK_DATA

After execution, we can see that the extracted financial data from Dropbox has been successfully structured and stored in Snowflake.

• Now Deploy the ETL Pipeline by clicking on the ‘Deploy as ETL Pipeline’ Button.

• Enter the details and click – ‘Save and Deploy’

You can review the pipeline execution logs or manage actions by navigating to the ‘ETL Pipelines’ section in the Unstract dashboard.

Expanding JSON Data into Multiple Columns in Snowflake: Best Practices

When processing unstructured data, Unstract generates structured results in JSON format. These JSON objects are stored in Snowflake as a single column, typically using the VARIANT data type.

While storing data in JSON format retains its hierarchical structure, querying specific fields directly from JSON objects can be complex. To simplify data analysis and improve query performance, Snowflake provides functionality to flatten JSON data into separate columns.

Storing JSON Data in a Snowflake Table

After processing bank statements through Unstract, the structured JSON output is stored in a Snowflake table called bank_statements. To facilitate better querying, we can create a new table named bank_data and store the JSON data in a VARIANT column:

CREATE OR REPLACE TABLE bank_data (

  src VARIANT

)

AS

SELECT PARSE_JSON(data) AS src

FROM unstract.unstract.bank_statements;

Querying Specific Fields from JSON Data

With the JSON data stored in bank_data, we can extract individual fields using dot notation. This approach allows us to reference and convert JSON attributes into structured table columns:

SELECT 

    src:customer_details.name::STRING AS "Customer Name",

    src:customer_details.phone::STRING AS "Phone Number",

    src:branch_details."Bank Name"::STRING AS "Bank Name",

    src:branch_details."Branch Name"::STRING AS "Branch Name",

    src:card_details."Card Type"::STRING AS "Card Type",

    src:card_details."Credit Limit"::STRING AS "Credit Limit",

    src:important_dates."Payment Due Date"::STRING AS "Payment Due Date",

    src:important_dates."Statement Date"::STRING AS "Statement Date",

    src:total_due_amount.value::STRING AS "Total Due Amount"

FROM unstract.unstract.bank_statements;

Optimizing Data for Analytics

By flattening JSON data into structured columns, Snowflake enables easier querying and integration with analytics tools. This approach ensures that business users and data analysts can efficiently extract and analyze relevant financial details from bank statements without needing complex JSON parsing.

With this method, businesses leveraging Unstract and Snowflake can seamlessly transform raw document data into structured, queryable insights—enhancing financial reporting, customer profiling, and automated reconciliation workflows.

Why Businesses Should Use Unstract for AI Document Processing

In an era where enterprises deal with massive volumes of unstructured data, AI-based document processing has become a necessity. Traditional manual methods are slow, error-prone, and inefficient, while conventional OCR solutions often fail to extract structured insights accurately. Unstract revolutionizes AI document extraction by offering a scalable, automated solution for businesses looking to streamline their document workflows.

Key Benefits of Unstract for AI Document Processing

• Efficiency – Automates the extraction of structured data from unstructured documents, reducing manual effort and speeding up processing times.
• Accuracy – Uses advanced AI document extraction techniques to extract essential details with precision, ensuring minimal errors.
• Scalability – Handles large-scale document processing with AI, making it ideal for industries such as finance, healthcare, legal, and logistics.
• Multi-Format Support – Processes PDFs, scanned images, handwritten forms, and complex documents while preserving structure and formatting.
• Seamless Integration – Connects effortlessly with enterprise systems like Snowflake, Dropbox, and other cloud databases to ensure smooth data flow.
• Automated Workflows – Enables organizations to set up end-to-end document processing AI pipelines for real-time data extraction and structured storage.

By leveraging AI-based document processing, businesses can eliminate the inefficiencies of manual data entry, improve compliance, and enhance overall decision-making by ensuring their documents are instantly searchable, structured, and ready for analysis.

Conclusion

Unstract is redefining the landscape of AI document processing by transforming raw, unstructured documents into structured, actionable insights. By automating data extraction, preserving document structure, and enabling seamless integration with enterprise systems, Unstract ensures businesses can process documents with unmatched speed and accuracy.

Organizations looking to streamline their AI-based document extraction workflows can easily integrate Unstract into their processes. Whether handling financial statements, contracts, or operational documents, Unstract’s intelligent document extraction AI capabilities ensure businesses gain faster processing times, reduced costs, and higher accuracy in their document workflows.

Get Started with AI-Powered Document Processing

Businesses can try Unstract today and experience first-hand how document processing with AI can revolutionize their operations. With easy integration, automated workflows, and scalable data extraction, Unstract provides a future-proof solution for enterprises looking to optimize their document management.

While you’re here, be sure to explore Unstract, our open-source, no-code LLM platform to launch APIs and ETL Pipelines to structure unstructured documents.

Even better, schedule a call with us. We’ll help you understand how Unstract leverages AI to help document processing automation and how it differs from traditional OCR and RPA solutions.