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How LLM-Powered System Streamlined Contract Analysis by 70%

MNDA

Enterprise/Corporation
Company Type
-, United States
Location
Project work
Engagement Model
6 - 10 people
Team Size
3 - 6 Months
Duration
$50K - $100K
Budget

About

A US-based company founded by former Amazon and Microsoft engineers was developing a SaaS platform for construction and legal teams to streamline contract analysis. With the LLM-powered solution developed, they achieved 70% faster processing and 90% higher accuracy across all document types.

Challenge, approach, and impact

LLM-Powered System for Contract Analysis

Development of an LLM-powered system to automate contract analysis workflows for construction and legal teams, achieving 70% faster processing and 90% higher accuracy.

Challenges Faced by the Client

Addressing slow, inconsistent document reviews during the development of a SaaS platform for contract analysis, leading to delays in processing client requests and quality issues.

Sub-Challenges

1. Model Training and Quality Assurance: Multiple training iterations required for accurate analysis. 2. Speed Optimization: Ensuring fast results without compromising quality. 3. Document Type Differentiation: Correctly identifying and analyzing various contract types.

Solution Implemented

Implementation of an LLM-powered engine with decision-tree logic for automated and structured contract analysis, providing tailored prompts for each contract type and delivering structured insights within seconds.

Sub-Solutions

1. Model Training and Quality: Conducting fine-tuning iterations for consistent results. 2. Speed Optimization: Refining prompt flow and decision tree architecture. 3. Document Type Differentiation: Guiding the LLM with a custom decision tree for contextual accuracy.

Results Achieved

Achieving 70% faster document analysis and 90% greater accuracy through LLM-driven automation, leading to improved speed, consistency, and scalability of document processing within the client's SaaS platform.

How we built

AI & ML Solutions
SaaS
Architecture
API
Enterprise Software

Testimonials

Anonymous

Dataforest

Verified Testimonial

Dataforest helped us integrate an LLM-powered document analysis engine into our SaaS platform, significantly improving both speed and accuracy. Their team quickly understood our domain challenges, delivered a scalable solution, and optimized performance for real-world usage. As a result, we reduced document processing time by 70% and achieved consistent, high-quality analysis across all contract types.

Team structure

Client team

Michael Johnson's avatar

Michael Johnson

Project stakeholder

Project stakeholder

Sarah Clark's avatar

Sarah Clark

Daily point of contact

Daily point of contact

The client stakeholders were working closely with the team at Dataforest

Agency team

3 x Machine Learning Engineer's avatar

3 x Machine Learning Engineer

Production

3 x Data Engineer's avatar

3 x Data Engineer

Production

2 x Business Analytics's avatar

2 x Business Analytics

Production

2 x Product Manager's avatar

2 x Product Manager

Production

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