Transforming Document Generation: DocuGen's Leap Forward with Featherlite

Introduction

The demand for quickly and accurately generating complex, customized documents is higher than ever. DocuGen understands the increasing need for businesses to generate complex, customized documents quickly and accurately. To address this demand, we aimed to harness the power of AI-driven solutions. However, achieving scalability while maintaining cost-effectiveness posed
significant challenges.

This case study explores how DocuGen’s partnership with Hexon who offered us their Featherlite Aurora model for long context document generation of upto 128k output tokens. We developed a scalable, efficient, and cost optimized platform for document generation for docugen.

Feature Enhancements

Internet Search Integration: Integration with Whoogle search (self hosted) allows users to pull in real-time information from the web, enriching documents with the latest, most relevant content.
Language Model Utilization: The chat functionalities in the platform is powered by Amazon Bedrock Sonnet 3 Haiku via LangChain along with Qdrant Vector DB. DocuGen provides robust natural language processing and understanding to improve document generation quality.
Chat Interface for Document Generation: A user-friendly chat interface enables users to create documents through conversational commands, making the process more intuitive and accessible

Copilot Integration: The Copilot feature assists users by offering intelligent suggestions and asks more questions from the user to clarify their ask and give better document responses.

The Challenge: Scaling Document Automation Cost-Effectively

DocuGen’s platform was designed to automate the creation of a wide range of documents, including personalized contracts, detailed reports, and bespoke Transforming Document Generation: DocuGen’s Leap Forward with Featherlite 4 communications. As demand grew, the company faced significant scalability challenges. Key issues included rising cloud service costs and the technical complexities of scaling AI models effectively. Adding to these challenges was the need to incorporate new features like internet search integration and a chat-based document generation interface. The goal was clear: Find a way to expand document automation services without proportionally increasing operational costs.

Hexon’s Solution

Central to the solution was the integration of Featherlite, utilizing Hexon’s finetuned Aurora model. This model was seamlessly integrated with DocuGen’s systems to enhance their document generation capabilities. 1. Amazon Bedrock A fully managed service, provided access to industry-leading foundation models and comprehensive capabilities for building generative AI applications. It enabled DocuGen to experiment with various foundation models.

2. Long Context & Humane Response Hexon implemented Rotary Position Embeddings (RoPE) scaling to extend the context length in their Featherlite Aurora model, achieving a capacity of 128k tokens which helped docugen to create long documents. The model was further fine-tuned with a custom curated dataset to enhance its human-like responses and ensure better system alignment. 3. Cost Savings Featherlite’s intelligent instance management system optimized the use of AWS spot instances, adjusting resource allocation based on real-time demand. This led to significant cost savings while ensuring efficient utilization of computational resources.

Impact and Outcomes

Scalability and Performance

Improved Throughput: Enhanced document generation throughput, handling higher volumes of requests without compromising performance.
Low Latency: Reduced latency in document generation, resulting in faster response times for end-users.

Cost Efficiency

Integrating Featherlite and Amazon Bedrock led to a 42% reduction in cloud costs for DocuGen.
Improved financial efficiency enabled DocuGen to invest in advancing its document automation technology and explore new innovations.

Customer Satisfaction

The integration significantly improved DocuGen’s service reliability and speed.
Resulted in faster document generation and enhanced accuracy.

Conclusion

The strategic partnership between DocuGen and Hexon’s Featherlite represents a significant advancement in document creation. By integrating Hexon’s fine-tuned Aurora model with DocuGen’s systems and leveraging Amazon Bedrock for other chat functionalities, DocuGen achieved an optimal balance of operational efficiency and cost-effectiveness. This case study highlights DocuGen’s success in optimizing document generation services and serves as a model for other companies facing similar AI-driven challenges.