Contracts are a gold mine of data, but manually extracting it is inefficient. Especially since contract data is often scattered across different systems or departments — making it hard to get a quick comprehensive view of contractual obligations.
Fortune 2000 companies typically manage 20,000-40,000 contracts simultaneously. So, consider the immense effort required to manually review dense, unstructured legal information and the (legal) expertise required to interpret the data within contracts.
Whether you're a legal professional minimizing risk, a procurement specialist optimizing spend, or a contract manager improving performance, automation can help change how you extract data from these contracts.
In this article, we will learn more about contract data extraction, challenges in extracting data from contracts, how to automate it, some popular methods of contract data extraction, and how it can streamline various stages of the contract lifecycle.
What is contract data extraction?
Contract data extraction is the process of automatically identifying and pulling out specific/relevant information from contracts or legal documents.
This process transforms unstructured contract text into structured data that is much more convenient to analyze. This also helps businesses to find and use key details hidden in their contracts, making it easier to understand and manage their agreements.
Here are a few use cases that largely focus on analyzing contracts along with examples of key contractual data:
| Use cases that require contract analysis | Key contract data that must be extracted |
|---|---|
| 1. Merger and acquisition | Party names, contract values, termination clauses, change of control provisions etc. |
| 2. Vendor management | Pricing terms, renewal dates, service level agreements (SLAs), liability clauses etc. |
| 3. Lease administration | Lease terms, rent amounts, renewal options, maintenance responsibilities etc. |
| 4. Employment contracts | Compensation details, non-compete clauses, benefits information, termination conditions etc. |
Automated contract data extraction uses technologies like OCR (Optical Character Recognition), NLP (Natural Language Processing), and machine learning to perform this extraction process without manual intervention.
The software scans documents to identify and extract critical data points (like end dates and payment terms), structuring these details into an organized digital format within seconds and routing that data into your systems for further processing and tracking.
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This automation transforms contracts from passive documents into strategic tools that provide insights to identify revenue risks, find savings, and empower better decisions. Lawyers and managers can easily search, analyze, and report on this data instead of digging through paperwork.
Proven IDP results!
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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.
Why is it challenging to capture data from contracts?
Given the legal nature of contracts, a high degree of accuracy is extremely crucial, leaving very little room for error.
But traditional data extraction solutions such as basic OCR or template-based extraction struggle with contracts for several reasons:
- Variety of formats: Contracts come in many different formats, layouts, and structures. Even the same type of agreement can vary significantly between organizations or departments.
- Complex language: Legal documents often use complex language, industry-specific terminology, and ambiguous legalese that can be difficult to parse programmatically.
- Inconsistent terminology: Different organizations may use varying terms or context-dependent information to describe the same concepts, making standardized extraction challenging.
- Critical accuracy requirements: Unlike many other document types, even small extraction errors in contracts can have significant legal or financial consequences.
- Unstructured data: While contracts may appear structured to humans, they contain mostly unstructured data from a machine perspective. 80% of enterprise data, including contracts, exists in unstructured formats that traditional systems struggle to process effectively.
Recent developments in machine learning have given rise to solutions capable of handling these complex data extraction tasks. These solutions use a combination of NLP, LLMs, and AI to read and understand contracts and identify key data within them.
These tools can be broadly grouped into two types:
- Specialized LLMs trained on legal data such as Harvey AI or Robin AI that are primarily used for legal review and contract analysis
- AI-powered rule-based intelligent document processing (IDP) solutions such as Nanonets that are mostly used for automating existing contract data extraction workflows
Types of contract data extraction approaches
As mentioned earlier, recent developments in machine learning have given rise to two main approaches for automated contract data extraction. Each addresses the challenges of contract complexity and unstructured data in different ways:
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
1. Significantly reduces time spent on contract review and data extraction
2. Handles various contract types and formats more effectively than rule-based systems
3. Identifies patterns and insights across large contract portfolios
4. Creates searchable databases of contract information that can be shared across teams and departments
1. Has a potential for misinterpretation, especially with complex or unusual clauses that it hasn't encountered before
2. Requires time/expertise to properly implement and fine-tune to maintain accuracy
3. May not seamlessly integrate with existing contract management systems and workflows
4. High initial investment for licensing, implementation and ongoing maintenance
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:
- Ensure the contract is in a digital, machine-readable format (e.g., PDF, Word, or plain text).
- 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).
- Create and fine tune prompts that instruct the LLM to extract specific data. For example: "Extract the following information from this contract:
- Parties involved
- Contract start date
- Contract end date
- Payment terms
- Termination clauses"
- Input the contract text and your prompts into the LLM. Some platforms may offer APIs for this step!
Look out for missing information or incorrectly extracted information.
- Use the results to further refine your prompts and improve accuracy.
Handling such exceptions might require custom prompts (just for these unique contracts) or routing them for good old manual review!
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).
1. Produces consistent structured data outputs - doesn't hallucinate!
2. Integrates with existing contract management systems and feeds extracted data directly into other business processes
3. Handles different document types beyond just contracts - can be used for a wider range of business use cases
4. Far easier to train or improve models to handle exceptions or corner cases
1. Struggles with complex legal language or "unseen" contract formats that require deep legal analysis
2. Doesn't generate summaries or can't explain contract terms
How to extract data from contracts using AI-based IDP software
Here's a quick guide on how to use Nanonets, a popular AI-based IDP software, to extract data from contracts. For this example, we'll extract data from a commercial lease agreement.
- Signup on Nanonets, login to your account, click on "New workflow" and create a "Zero training model".
- Specify the data points you want extracted from your contract. For example, here are the data points I want to extract from a sample commercial lease agreement:
- Landlord
- Tenant
- Landlord address
- Tenant address
- Commencement date
- Termination date
- Upload your contract and wait for a few seconds. Nanonets AI will display the key contractual data like so:
- You can correct or modify the data extracted by the AI and it will "learn" from those corrections/modifications and keep getting better.
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:
Proven IDP results!
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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.
How automated contract data extraction using IDP works
Let's take a closer look at how AI-powered IDP solutions like Nanonets work to extract and process contract data. The process begins by ingesting contracts into the system and ends with the software automatically extracting key data points into structured fields and triggering downstream workflows.
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The essential data points that can be extracted from contracts include end dates, party names, pricing terms, liability limits, renewal terms, service level agreements, and more.
Here's a step-by-step look at how it works:
1. Ingest contracts
The process begins by ingesting paper or digital contracts into the system. Solutions like Nanonets can scan and digitize printed contracts using OCR technology. Users simply upload documents directly or automate importing from cloud storage or email for existing digital agreements.
2. Extract data using AI
The software analyzes documents to identify relevant information such as parties, dates, terms, and more and converts them into searchable digital text. As the system processes more agreements, the AI continuously improves, learning to recognize critical data points better.
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3. Structure data
The extracted information is structured into labels and categories, ensuring it's accurate and properly formatted. Data like names, addresses, and dates are validated and standardized. Unstructured free text is tagged and classified based on meaning.
4. Validate data and route for approval
The software automatically validates the extracted data using predefined rules to catch any errors or inconsistencies. For example, it can flag documents for human review where the values are beyond expected ranges or dates that are in the past.
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Based on business rules, the data is then routed to the appropriate teams for approval. For example, renewals due within 60 days can automatically notify procurement to review pricing. Agreements with changes to terms over a threshold value may be routed to legal for additional scrutiny before approval.
4. Export data
Once data has been successfully extracted, structured, validated, and approved, it can be exported as CSV or JSON files for use across your systems. Structured contract metadata can integrate directly with databases, analytics tools, and existing workflows.
5. Trigger workflows
By connecting extracted data to workflows, contract management processes can be automated. Data can trigger notifications of renewals, deadlines, approvals, and more. Workflows route contracts, tasks, and data to the right people when needed.
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While the exact process may vary based on your specific use case and solution, the core principles remain the same. Automated intelligent data extraction takes the manual effort out of making sense of contracts.
This transforms disconnected documents into structured data that provides visibility into risks, obligations, and opportunities to drive savings and revenue. Rather than reacting to contracts after signing, businesses can proactively manage agreements as strategic tools.
Proven IDP results!
▼
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.
How does automated contract data extraction using IDP address common workflow challenges?
Do you want your legal team spending their time on high-value work or manual data entry? Would you rather have procurement negotiate better deals or chase down paperwork? Is your contract management team focused on strategic initiatives or busy searching for files?
Do you often deal with late payment penalties or missed opportunities due to a lack of visibility into contract data? If so, you're not alone. Many organizations need help with these challenges, leading to wasted time, resources, and revenue.
Automated contract data extraction using IDP addresses these issues by digitizing and structuring critical information from agreements.
Challenge 1: Manual data entry is time-consuming and error-prone
Manually reviewing contracts to extract key data points is tedious and error-prone. It can take hours to locate and accurately capture critical details like parties, dates, terms, and values from a single contract. Multiply that effort across hundreds or thousands of agreements, and the burden becomes immense.
IDPs like Nanonets eliminate this manual task. The software quickly scans contracts, pulls the critical data, and populates it into the correct fields. This saves significant time while ensuring that data is captured accurately and completely.
Challenge 2: Scattered contract data makes it difficult to track obligations and opportunities
When contract data is locked away in individual documents scattered across various storage locations, keeping track of important details can be challenging. Renewals are missed, compliance issues go unnoticed, and renegotiation opportunities slip through the cracks.
IDPs make key contract details centralized, organized, and easily searchable. Teams can instantly access the necessary information and set up alerts for important dates and events. This visibility enables more proactive contract management.
Challenge 3: Inability to integrate contract data with other systems
Contracts hold a wealth of essential business data. However, it's not actionable if that data is trapped in unstructured documents. Manually transferring contract data into other systems is time-consuming and introduces risks of errors.
IDPs captures contract data and structures it in a usable format. This enables contract data to seamlessly integrate with other business tools and workflows, such as CRMs, ERPs, and procurement systems. Contract data becomes connected to business processes, driving efficiency and informed decision-making.
Challenge 4: Difficulty handling large volumes of contracts
As businesses grow, so does the number of contracts they must manage. Manual contract management processes quickly become overwhelmed by the increasing volume. This leads to delays, inconsistencies, and increased risk.
Automated data extraction using IDPs enables businesses to scale contract management efficiently. The software can process vast numbers of contracts quickly and consistently without increasing headcount. This empowers teams across the organization, from Legal to Procurement to Sales, to stay on top of their contract responsibilities and make data-driven decisions.
Challenge 5: Ensuring data accuracy and consistency
Manually extracting data from contracts is prone to human error. Small mistakes, like a mistyped date or a skipped clause, can have significant consequences. Inconsistent data entry across team members also makes maintaining a single source of truth difficult.
IDPs ensures data accuracy and consistency. The software applies the same rules and validation checks to every contract, eliminating the risk of human error. Advanced solutions like Nanonets allow users to train custom AI models to capture company-specific clauses and data points accurately, further ensuring precision and reliability.
Challenge 6: Lengthy contract cycles
The contract lifecycle involves many time-consuming steps, from drafting to negotiation to execution. Manually transferring data between systems, sending documents for review, and chasing approvals all extend the contract cycle. These delays slow time to revenue and can frustrate stakeholders.
Automation accelerates contract cycles by eliminating data entry and document hand-offs. Extracted contract data can automatically be routed for approval based on rules like contract value thresholds. Alerts can notify relevant parties when action is required. Data can flow directly into systems like CRM to trigger the next steps after execution.
Challenge 7: Reactive contract management
Many organizations take a reactive approach to managing contracts, only referring to them when issues arise. This leads to missed milestones, forgotten renewals, and lost revenue opportunities. Contract managers are left scrambling to resolve urgent problems at the last minute without proactive notifications.
Automated data extraction enables organizations to take a proactive approach to contract management. By capturing key dates and terms upfront, IDPs can generate automatic alerts for upcoming renewals, expirations, and other important events. This empowers teams to get ahead of contract issues before they become problems.
Challenge 8: Lack of contract performance insights
Contracts contain valuable data that can provide insights into vendor performance, risk exposure, revenue trends, and more. However, it's difficult to aggregate and analyze when contract data remains trapped in documents. This lack of visibility prevents organizations from optimizing contract performance.
Automation unlocks contract data for analysis by extracting and structuring key details. Metadata can be exported into analytics tools to track KPIs, identify bottlenecks, and surface improvement opportunities. With contract data at their fingertips, organizations can make data-driven decisions to maximize contract value.
IDP is critical to accelerating contract velocity and maximizing performance. It transforms legal, procurement, and contract management processes by digitizing and integrating contract data directly into your workflows. It eliminates the tedious busywork that keeps teams from focusing on strategic priorities.
How can Nanonets help transform your contract data extraction workflows?
Looking for an effective IDP solution to address these challenges? Nanonets' AI-based contract data extraction is the answer.
Nanonets is a powerful, no-code IDP platform that enables businesses to automate their contract data extraction processes. With Nanonets, you can quickly train custom AI models to accurately capture critical data points from your contracts, regardless of format or complexity. The platform seamlessly integrates with your existing systems, allowing you to streamline workflows and make data-driven decisions.
Let's explore how Nanonets can help:
Effortless contract import: Automatically import contracts from email, Google Drive, Dropbox, or contract management systems. You can even set up triggers to import the contract documents as soon as they arrive. You can handle various formats, such as scanned PDFs, digital documents, and images.
Advanced AI models: Nanonets lets you train custom AI models to extract data fields unique to your contracts accurately. This means the platform adapts to your contract templates and clauses, not the other way around. You can also use pre-trained models to identify and capture key data points accurately.
Human-in-the-loop validation: Extracted data is presented to legal/contract teams for review within Nanonets. Users can quickly correct discrepancies, helping the AI learn and improve. It ensures a high level of data accuracy and trust.
Scalable processing: As your contract volume grows, Nanonets can keep pace. The platform can handle thousands of contracts without breaking a sweat, ensuring you never fall behind on data extraction.
Complex contract handling: With advanced AI, Nanonets can extract data from even the most complicated contracts, including scanned PDFs, images, and contracts with multiple layouts or formats. It adapts to your evolving contract needs.
Data enrichment: Enrich extracted data with information from CRM, ERP, and other systems. This lets you gain a 360-degree view of your contracts and make informed decisions. Nanonets also allow you to validate extracted data against external sources for added accuracy.
Seamless workflow integration: Nanonets fits right into your existing contract workflows. Extracted data can be automatically exported to your contract lifecycle management system, CRM, ERP, or any other database, eliminating manual data entry. It helps break down data silos and enable contract intelligence to flow freely.
Automated risk flagging: Nanonets can automatically flag review contracts based on predefined rules, such as missing clauses or non-standard terms. This helps ensure compliance and mitigate potential risks.
Enhanced contract intelligence: With Nanonets, you can unlock valuable insights from your contract data. Quickly analyze contract metrics, identify performance trends, and make data-driven decisions to optimize contract outcomes.
Robust data security: Nanonets employs industry-leading security measures, including advanced encryption and secure data handling practices, to keep sensitive contract data safe and compliant. Granular access controls ensure sensitive contract information is only accessible to authorized users.
Conclusion
Automated contract data extraction transforms unstructured agreements into strategic assets by capturing critical information efficiently. For successful implementation, start with clear objectives and a focused pilot project. Prioritize high-value data points like key dates and financial terms, and develop a robust validation strategy with processes for handling exceptions.
Integration with existing systems maximizes value, while comprehensive training ensures adoption. Track and communicate metrics like time saved and accuracy rates to demonstrate ROI.
Whether you choose specialized LLMs or AI-powered IDP solutions like Nanonets, select technology that aligns with your specific needs. As contract complexity increases, automated extraction has become essential for organizations seeking to make data-driven decisions and maximize the value of their contractual relationships.