How LLM Improves Document Processing for SaaS

How an LLM-Powered System Streamlined Contract Analysis by 70%

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. They needed to speed up and scale document processing. With the LLM-powered solution we developed, they automated analysis workflows, achieving 70% faster processing and 90% higher accuracy across all document types.

AI-Ready Data Infrastructure

Generative AI

LLM Analysis Engine

faster document processing speed

higher analysis accuracy

A US-based company founded by former Amazon and Microsoft engineers, providing AI-powered contract analysis and document management solutions for construction and legal teams. Their platform, Brief, helps US clients organize project documents, predict risks, and access critical information instantly—integrating seamlessly with tools like Procore and SharePoint.

OpenAI

Langchain

AWS

Docker

Qdrant

THE CHALLENGE

The Client Faced Slow, Inconsistent Document Reviews While Developing Their SaaS Platform for Contract Analysis

The client was developing a SaaS platform to streamline contract and document analysis for US construction and legal teams. However, their existing manual review process was slow, inconsistent, and difficult to scale, causing delays in client request processing and quality issues.

To enhance their product’s efficiency and value, they needed to integrate an LLM-based solution that could automatically analyze entire document sets, extract key insights, and deliver faster, more reliable results within their SaaS environment.

Model Training and Quality Assurance

Achieving the required accuracy for diverse legal and construction documents demanded multiple training iterations and prompt engineering refinement.

Speed Optimization

The LLM needed to deliver fast analysis results without compromising quality, ensuring seamless integration into the client’s SaaS workflow.

Document Type Differentiation

The system had to correctly identify and analyze different contract types (e.g., NDAs, change orders, project agreements), each requiring unique logic and prompts.

Explore how LLM-powered automation can accelerate your document review and boost accuracy.

Build your PoC within 2 weeks — get pricing today.

THE SOLUTION

Implementing an LLM-Powered Engine with Decision-Tree Logic for Automated, Structured Contract Analysis

To address these challenges, we implemented an LLM enhanced with decision-tree logic that applied specific prompts for each contract type. Once a user uploaded a document, the system automatically analyzed it—extracting key details such as NDA clauses, dates, and obligations—and delivered structured insights within seconds.

‍

For Model Training and Quality

We conducted multiple fine-tuning iterations and testing cycles to achieve consistent, high-quality results across various document types.

For Document Type Differentiation

The LLM was guided by a custom decision tree that mapped document categories to tailored prompt templates, ensuring contextual accuracy for every analysis.

For Speed Optimization

Our engineers refined the prompt flow and decision tree architecture, enabling real-time document analysis without performance lag.

THE RESULT

Achieving 70% Faster Document Analysis and 90% Greater Accuracy Through LLM-Driven Automation

The implementation of the LLM-based analysis engine significantly improved the speed and consistency of document processing within the client’s SaaS platform. By automating contract review workflows and optimizing the decision tree logic, the client achieved measurable efficiency gains and higher end-user satisfaction.

Key Outcomes:

Document analysis speed increased by up to 70%, reducing review time from several minutes to seconds per file.
Accuracy and consistency improved by over 90%, minimizing human error and ensuring reliable insights across document types.
Scalability enhanced — the system now supports batch analysis of large document sets without performance degradation.
Seamless integration with the client’s SaaS product, enabling automated analysis immediately after document upload.

Overall, the solution empowered the client to process more client requests in less time, strengthening the product’s value proposition and positioning it as a next-generation platform for intelligent document analysis.

70%

faster document processing speed

90%

higher analysis accuracy

How an LLM-Powered System Streamlined Contract Analysis by 70%

The Way We Deal with Your Task and Help Achieve Results

Step 1 of 5

Web development discovery

It's a good time to get info about each other, share values, and discuss your project in detail. We will advise you on a solution and help you understand if we are a perfect match for you.

Step 2 of 5

Discovering And Feasibility Analysis

One of our core values is flexibility. Hence, we work with either one-page high-level requirements or a whole pack of tech docs. In AI demand forecasting case studies, there are numerous models and approaches, so at this stage, we perform a set of interviews to define project objectives. We elaborate and discuss a set of hypotheses and assumptions. We create a solution architecture, a project plan, and a list of insights or features to achieve.

Step 3 of 5

Solution Development

The work starts with data gathering, data cleaning, and analysis. Feature engineering helps to determine your target variable and build several models for the initial review. Further modeling requires validating results and selecting models for further development. Ultimately, we interpret the results. Nevertheless, demand forecasting solution modeling is a process requiring many back-and-forth iterations. We are result-focused, as it's also one of our core values.

Step 4 of 5

Solution Delivery

AI demand forecasting solutions can be a list of insights or models that consume data and return results. Though we have over 15 years of expertise in data engineering, we expect the client's participation in the project. While modeling, we provide midterm results so you can always see where we are and provide us with feedback. By the way, a high level of communication is also our core value.

Step 5 of 5

Support And Continuous Improvement

We understand how crucial the solutions that we code for our clients are! We aim to build long-term relations, providing guarantees and supporting agreements. Moreover, we are always happy to assist with further developments, and statistics show that 97% of our clients return to us with new projects.

Success stories

All Success Stories

40% Less Manual Work, Faster Decisions: AI SaaS Platform Transforms Construction Operations

EZeBld, a construction SaaS provider, replaced manual workflows and fragmented tools with an AI-driven platform built by DATAFOREST. The solution automates routine tasks, predicts budget and timeline risks, and delivers instant answers via an AI WhatsApp chatbot. It results in 40% less manual work, faster approvals, and full real-time visibility.

40%

reduction in manual workload through AI automation

95%

View case study

40% Less Manual Work, Faster Decisions: AI SaaS Platform Transforms Construction Operations

AI SaaS Platform Transforms Construction Projects

AI Platform Revolutionizing Healthcare Insights

A UK healthcare market intelligence company partnered with Dataforest to drive digital transformation. We developed an AI-powered enterprise management platform that automated core processes such as data collection and report generation with deep analytical insights. With dynamic web scraping, AI-based deduplication, and GenAI data enrichment, the solution cut 9,600+ manual hours monthly and doubled productivity—delivering significant operational gains.

9,600+

hours/month of manual work eliminated

increase in overall productivity

View case study

AI Platform Revolutionizing Healthcare Insights

LLM-Powered Recommendation System

An Israeli startup is transforming U.S. service providers' personalized offerings. Dataforest scaled the project from prototype to a full web app with advanced ML, LLMs, and RAG fine-tuning. Managing 100,000+ products for 50,000+ customers, it delivers precise recommendations and revenue forecasts, maximizing sales opportunities

<1 min

tailored recommendations delivery

100,000+

products supported by the platform

View case study