GenAI Solutions for Contract Template Generation and Summarization
Enhancing contract workflows for startups and SMEs with context-aware automation and advanced document analysis.
About
The Client
Our customer provides a unified platform to manage, sign, and store all contracts, integrating intelligence and automation to streamline contract management for startups and SMEs globally. Features like customizable templates, digital signing, and comprehensive contract repositories, drive efficiency and accuracy in legal document handling.
The
Challenge
As a growing business, manually onboarding and maintaining contracts at scale can have severe implications, from non-compliance and poor delivery to litigation and financial losses.
In the legal industry, contracts are often lengthy and complex, involving multiple parties, clauses, and obligations making it cumbersome and time consuming activity.
Organizations and individuals face challenges including:
Creating templates for legal documents to make it efficient and compliant as per the industry laws
Summarizing large documents to expedite content to review, negotiate, and manage contracts
Finding missing clauses and errors within contracts.
Identifying differences between document versions to ensure accuracy and consistency.
What Forgeahead Did
Forgeahead implemented an LLM-powered solution using AWS Bedrock to address the challenges
Template Generation
Template generation with LLM is used to create templates for legal documents to make it increasingly efficient and compliant as per the industry laws, closing deals faster, and eliminating business risks.
Contract Summarization
This can cut through the noise and generate a contract summary as a short outline that surfaces the key information from a contract.
Contract Classification
Designing and training a GEN AI model for document classification, clause identification and can accurately identify compliance issues and provide actionable recommendations.
LLM Fine-Tuning
Utilized AWS Bedrock to fine-tune pre-trained LLMs (e.g., GPT-3, BERT) on a dataset of 20,000 legal documents.
Deployment
The trained model was deployed as a custom endpoint using AWS Bedrock for user accessibility.
Optimization
Tuned hyperparameters and experimented with different model configurations for optimal performance.
Monitoring and Feedback
Continuously monitored performance and gathered user feedback to refine the solution.
0%
reduction in review time
Optimized Efficiency
0%
accuracy rate
Minimized Risk
0%
better decisions
Enhanced Decisiveness
0%
better decisions
Reduced Legal Risks
0%
favorable agreements
Improved Contracts
0%
agreements met legal standards
Reduced Non-Compliance
Technology Stack
Python
AWS Bedrock
AWS Lambda
What’s next?
Developing a Secure, Multi-Tenant SaaS Platform for Modern Learning
