AI-powered contract management platform for enterprises
Automated PDF data extraction and compliance validation for high-volume contracts
Project highlights
Solution
Key features of the product in detail
Text and Table Extraction from PDF
The platform provides robust support for extracting content from both native and scanned PDF documents. It can accurately identify and process both structured and unstructured text across multi-page documents, preserving the layout and handling complex formatting. Optical Character Recognition (OCR) is used to convert scanned content into readable and extractable text, ensuring high accuracy even in lower-quality scans.
Intelligent Data Extraction from Text
Using large language models (LLMs), the system can identify and extract critical information buried within free-form text, such as cost details, contractual obligations, deadlines, and legal terms. This is essential for working with contracts that do not follow consistent formatting, allowing for flexibility and adaptability across different document types.
Advanced Table Recognition and Parsing
For tables embedded in PDF files, including those with non-standard layouts, the platform employs enhanced algorithms to correctly parse key-value relationships and tabular structures. In cases where OCR struggles, LLMs are used to interpret and reconstruct the intended meaning of complex tables.
Functionality Support for a Variety of Contract Types
The system is designed to work with a wide range of contract types and document structures, including those used in enterprise and government settings. It adapts to different legal templates, industry-specific formats, and terminology variations, ensuring broad applicability without the need for customization.
Support Amendment Comparison and Change Detection
Users can upload amendments to existing contracts, and the system automatically identifies and highlights changes. This feature reduces the risk of overlooking critical updates and simplifies the process of validating whether revised documents remain compliant with internal policies and external regulations.
Seamless Integration and Secure Data Flow
Built on Microsoft Azure, the platform integrates directly with the client’s infrastructure, including ERP systems like SAP. A secure data pipeline ensures reliable communication between systems while maintaining strict data protection standards required in regulated environments.
User-Friendly Interface and Robustness
The platform’s interface is intuitive and accessible, designed for use by both legal and operational staff. It includes built-in mechanisms for error handling and exception management, enabling it to deal gracefully with corrupted files, irregular formatting, or unreadable text.
Business outcome
Achievion successfully delivered a fully-functional platform that met all functional and non-functional requirements within the client’s budget and infrastructure constraints. The solution significantly reduced manual effort in reviewing large and complex contracts, accelerated processing time, and improved data accuracy.
It also ensured better compliance through automated validation against regulatory frameworks. The system’s scalability, security, and seamless integration with the client’s Azure-based ecosystem made it a reliable foundation for enterprise-wide adoption. The system delivery demonstrated the viability of using ready-made AI services to solve real-world problems efficiently and cost-effectively.
Timeline
- Developed project architecture and infrastructure
- Created platform UI/UX design
- Documented requirements
- Contract field extraction
- Table extraction
- Text & metadata extraction
- Contract details extraction
- Clause extraction
- Missing fields handling
- Text and table extraction from PDF
- Intelligent data extraction from text
- Advanced table recognition and parsing
- Amendment comparison and change detection
- Seamless integration with SAP and secure data flow
- User-friendly interface
Team
Tech Stack
AI/ML:
Backend:
Frontend
DevOps
can solve your business problem.
