How to extract data from contracts: A practical guide

Your contracts are a goldmine of critical data, but manually extracting it is slow, expensive, and dangerously risky. It’s a problem every legal, finance, and procurement team has, but few have solved elegantly.

According to a PwC report, large organizations manage between 20,000 and 40,000 active contracts at any given time. The sheer effort required to manually review that volume of dense, unstructured legal information is staggering. The data within these documents, key dates, obligations, renewal terms, and liability limits, is the lifeblood of your business relationships. But it’s often trapped, scattered across different systems and departments.

In this article, we’ll move beyond the contract data extraction basics. We’ll dissect the specific contract data points that matter, explore the spectrum of technologies available to extract them, and provide a framework for choosing the right approach for your business.

What data is hiding in your contracts, and why it matters

Effective contract management starts with knowing what to look for. "Contract data" isn't just one thing; it's a collection of specific metadata points, each tied to a critical business function. Automating the extraction of these fields is the first step to transforming contracts from static documents into insightful assets.

Here are some of the most crucial data points and the value they reveal:

Source

Manually tracking these details across thousands of documents is a recipe for failure. The real value comes from extracting this information at scale and making it searchable, reportable, and actionable. Doing this reliably usually requires an end-to-end automated data extraction pipeline that connects OCR/VLMs, clause detection, validation, and exports.

The evolution of contract data extraction

Businesses have been trying to automate contract data extraction for a long time. Let's examine the various generations of technology adopted and how each solved different pieces of the puzzle.

• Rule-based extraction (Regex): For highly standardized documents, using regular expressions to find patterns (like dates in a DD-MM-YYYY format) can be fast and effective. However, it's incredibly brittle. A slight change in document format breaks the rules, making it unsuitable for varied contract types.
• Traditional OCR and template-based ML: Optical Character Recognition (OCR) turns images into text, but without understanding context. Early machine learning systems from vendors like AWS Textract built on this by learning "zones" in a document (e.g., "the invoice number is always in the top right corner"). This fails the moment a contract deviates from the trained template.
• Modern AI and LLMs: The arrival of Large Language Models (LLMs) like those powering GPT marked a significant leap. These models can understand language and context, making them "template-agnostic." However, they also introduced a new set of sophisticated challenges. The legal domain is a classic "zero-resource" problem for AI. As academic research by Zin et al. highlights, creating the high-quality, annotated legal data needed to train a model from scratch is prohibitively expensive, with costs for benchmark datasets running high. This makes zero-shot or zero-training models not just a convenience, but a necessity.

The latest evolution is the move towards agentic AI. Instead of a single model performing a single task, an agentic system can break down a complex problem (like "process this new vendor contract") into a series of logical steps.

This approach moves from simple pattern matching to a form of automated reasoning. This reasoning can be further enhanced by providing the system with explicit AI Agent Guidelines. These special instructions tell the model how to handle unique business rules, such as vendor-specific extraction logic or how to filter irrelevant pages from a document. This could become critical for handling the complexity of real-world contract workflows.

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The two modern paths to automation: Which is right for you?

Today, businesses looking to automate contract data extraction typically face a choice between two powerful but distinct types of AI solutions. Choosing the right one depends entirely on the problem you're trying to solve.

a. Specialized LLMs for legal analysis

Most LLMs and generative AI-based solutions are prone to hallucinations - especially when it encounters unknown data.

That's the reason you can't use Chat GPT or Claude with absolute certainty for legal reviews or contract analysis.

On the other hand, LLMs trained on legal data and case law materials have a deeper and much better understanding of legal terminology and contract structures, and are less likely to hallucinate or make stuff up.

Since such LLMs are trained on large data sets of legal data, they have excellent contextual understanding. They can even understand clauses within the larger context of a contract.

They are ideal for contract analysis, legal research, and legal document drafting; saving time that would otherwise be spent on manual search. Here are a few examples of the top LLMs trained on legal data or AI contract review software:

• Harvey AI: A legal-focused AI using GPT technology
• Robin AI: A co-pilot for legal tasks
• LEGAL-BERT: A BERT-based machine learning model trained on hundreds of thousands of legal documents
• Lexis+ AI: A personalised legal AI assistant
• Casetext's CoCounsel: An AI legal assistant powered by GPT-4

How to extract data from contracts using LLMs trained on legal data

Here's a generic tutorial on how to use LLMs trained on legal data such as Harvey AI or Robin AI to extract data from contracts:

1. Ensure the contract is in a digital, machine-readable format (e.g., PDF, Word, or plain text).
2. Identify the specific data points you need to extract (e.g., parties, dates, terms, clauses) and specify a structured format for the output (e.g., JSON, CSV).
3. Create and fine tune prompts that instruct the LLM to extract specific data. For example: "Extract the following information from this contract:
   1. Parties involved
   2. Contract start date
   3. Contract end date
   4. Payment terms
   5. Termination clauses"
4. Input the contract text and your prompts into the LLM. Some platforms may offer APIs for this step!

5. Use the results to further refine your prompts and improve accuracy.

b. Contract data extraction with AI-powered IDP software

More often than not, businesses looking for a contract data extraction solution, require something that can fit into their existing setup or workflows.

Ideally no one prefers a solution that requires them to ditch an existing contract management system or make a ton of modifications to existing processes.

Rule-based IDP solutions do a great job of automating contract data extraction workflows without disturbing existing processes. They serve as an ideal middleware between unstructured contracts and contract management systems (or legal ERPs).

How to extract data from contracts using AI-based IDP software

A modern AI-based IDP software platform allows you to build a powerful and reliable process without needing a team of developers.

Here’s how you can set up a robust contract data extraction workflow using Nanonets as a practical example:

Step 1: Define your fields in a zero-training model.

Start by creating a new workflow and selecting a "Zero training model." In the "Manage Labels" section, define the specific fields you need to extract (e.g., Landlord, Tenant, Commencement Date, Liability Cap). For each field, provide a clear, concise description. This prompt-based approach guides the AI, telling it exactly what to look for and in what context, without needing any pre-labeled examples.

Step 2: Configure your automated workflow.

In the Workflow tab, connect the building blocks of your process.

• Import: Set up an automated import from sources like email, Google Drive, or SharePoint.
• Data actions: Add post-processing steps to clean and standardize the extracted data. For example, you can format all dates to a YYYY-MM-DD standard or use a lookup table to match a vendor name to a vendor ID in your database.
• Approvals: Create rules to flag documents for human review. For instance, "Flag if Governing Law is not 'Delaware'" or "Flag if Renewal Term is 'Automatic'."
• Export: Connect the workflow to your destination system, whether it’s an ERP like SAP, a CRM like Salesforce, or a database via webhook.

Step 3: Process your first batch and verify.

Upload a diverse set of 10-20 contracts to test the workflow. For each document, review the AI's extractions. If the model misses a field or extracts it incorrectly, simply draw a box around the correct text and assign the right label. This human-in-the-loop verification is crucial for fine-tuning the model.

Step 4: Approve and let the AI learn.

Once you've corrected a document, click "Approve." Our Instant Learning model uses this feedback immediately to improve its accuracy on the next document it sees. This continuous learning loop ensures the model adapts to your specific contract types and gets smarter over time.

Step 5: Scale with confidence.

Once the model consistently achieves high accuracy on your test documents, you can roll it out across your entire contract repository. The automated workflow will handle the volume, flagging only the true exceptions for your team to review, freeing them to focus on high-value strategic work.

IDP solutions like Nanonets also allow you to build end-to-end automated workflows on top of robust data extraction capabilities. You can:

• Auto-capture incoming contracts via email, hot folders or API
• Refine the extracted data through custom data actions
• Customize the final structured output
• Set up approvals or validations for the extracted contract data
• and finally export it to a downstream contract management software or ERP

Here's a quick overview of these features on Nanonets:

If your primary goal is legal research and analysis, a legal AI is a powerful tool. If your goal is to automate and scale a business process that relies on contract data, a workflow automation platform is the more practical and effective solution.

Under the hood: Solving the "Too Long; Didn't Read" problem for AI

A significant technical hurdle for any AI processing lengthy contracts is the "token limit"—the maximum amount of text a model can analyze at once. Many contracts easily exceed this limit.

The simplest solution, chunking, involves breaking the document into smaller pieces and analyzing them independently. However, research shows this often fails because it severs long-range dependencies. A clause on page 3 might be defined by a term on page 27. If the AI only sees one chunk at a time, it can't make that connection, leading to inaccurate or incomplete extractions.

A more sophisticated approach, and one central to modern extraction platforms, is query-based summarization. Before feeding the contract to the main LLM, a faster, more efficient model performs a preliminary scan. It retrieves only the sentences and paragraphs that are most relevant to the specific data points you're looking for (e.g., anything related to "payment," "termination," or "liability"). This creates a shorter, highly relevant summary that fits within the token limit while preserving the necessary context for accurate extraction.

Putting it into practice: A contract data workflow in action

Our AI-based workflow approach enables us at Nanonets to help companies automate the processing of thousands of complex documents, saving them time, money, and countless headaches.

Example scenario 1: Prepping for an audit

An investment firm needs to review the "Indemnification" and "Governing Law" clauses in all of its partnership agreements. Instead of having paralegals spend weeks manually searching through PDFs, the firm uses Nanonets to build a custom model. They upload their entire contract repository, and within hours, they have a structured spreadsheet containing the exact clauses from every single document, ready for analysis.

Example scenario 2: Automating vendor credentialing and risk management

Suppose a healthcare logistics company needs to verify credentials for its network of transportation vendors, a process involving over 16 different document types per vendor, including vehicle registrations and insurance policies. We recently worked with US-based SafeRide Health to automate this complex, high-volume task. We first classified each submitted document (e.g., distinguishing an insurance policy from a driver's license). Then, our model extracts critical data points from each, such as "Insurance Coverage Amount" and "Policy Expiration Date" from the insurance contracts. Custom approval rules can then automatically flag any vendor whose insurance is below the required minimum or nearing expiration, enabling proactive compliance and risk management at scale.

Example scenario 3: Accelerating M&A due diligence

During an acquisition, a corporate development team has one week to review 2,000 customer contracts from the target company. Their primary concerns are identifying "Change of Control," "Assignment," and "Exclusivity" clauses. Manually, this is impossible. Using a workflow platform, they define these three clauses as the key fields to extract. The system processes the entire data room overnight, producing a dashboard that flags all contracts with restrictive clauses, allowing the legal team to focus their limited time on the 50 highest-risk agreements.

Example scenario 4: Streamlining real estate lease abstraction

A commercial real estate firm manages 500 properties, each with complex lease agreements. They need to track critical dates like "Rent Commencement," "Lease Expiration," and "Option to Renew." Using Nanonets, they create a model specific to lease agreements. The platform extracts these dates and other key financial terms, then pushes the structured data directly into their property management software (like Rent Manager), automating rent roll reporting and renewal notifications.

Best practices for a successful implementation

Embarking on a contract data extraction project can seem daunting. Here are five best practices to ensure success:

1. Start with a high-value pilot project. Don't try to boil the ocean. Begin with a single, well-defined problem where automation can provide a clear win. A great starting point is often automating the extraction of renewal and expiration dates to prevent unwanted costs.
2. Define your data schema upfront. Before you process a single document, work with stakeholders from legal, finance, and procurement to define exactly what information you need to extract and what you will do with it. A clear plan prevents wasted effort.
3. Involve stakeholders early and often. The most successful projects have buy-in from all relevant teams. The legal team can validate the accuracy of clause extraction, while the finance team can confirm that the payment terms are being correctly routed to the accounting system.
4. Plan for the exceptions. No AI is perfect. A robust workflow must include a human-in-the-loop process for handling exceptions. Use rules to automatically flag documents with low-confidence scores or unusual values for expert review. This builds trust in the system.
5. Measure and communicate your ROI. Track key metrics from the start. How many hours are you saving per week? Have you reduced payment errors? Have you identified cost-saving opportunities by renegotiating contracts you would have otherwise missed? Communicating these wins builds momentum for broader automation initiatives.

Proven IDP results!

▼

Join for a demo to see how Nanonets has helped businesses like yours automate document processing and achieve tangible results. Our experts will also work with you to understand your unique needs and tailor a solution that maximizes your ROI.

📅 Schedule a Demo

Final thoughts: Stop reading contracts and start using them

The goal of contract management isn't to become an expert at reading documents; it's to use the information within them to run your business more effectively.

Manual processes are a liability. Early technologies were too rigid and complex. A modern workflow approach, combining powerful, template-agnostic AI with practical, rule-based human oversight, is the only way to tame the paper dragon and scale your operations.

Stop letting your contracts sit in a digital filing cabinet. It’s time to turn them into your most valuable data asset.

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