0M
Total Cost savings
0x
Faster Project Development
0%
Reduced Data Retrieval

Overview

A mid-sized pharmaceutical company in the USA, specializing in developing cutting-edge therapies for chronic illnesses, faced challenges in managing vast research data and experiments. The R&D team required an efficient system to catalog all experiments and associated data, ensuring quick and reliable data retrieval to support ongoing innovative research efforts.

Pharmaceutical Company

The Challenge

The pharmaceutical company’s R&D team faced delays due to an inefficient MySQL database, needing a robust and secure system for better data management and retrieval. They initially faced a daunting scenario with a project budget of $1.5 million and an estimated duration of six months. The recruitment and retention of skilled Python and NextJS developers and capable project managers posed a significant hurdle, contributing to potential delays and increased costs. Traditional IT solutions were similarly expensive and failed to provide accelerated timelines.

The Solution

SuperAGI utilized SuperCoder to develop a custom RAG (Retrieval-Augmented Generation) software using an open-source LLM, securely deployed on the company’s private servers. Using Django for backend and NextJS for frontend, the solution seamlessly integrated with existing systems. SuperCoder made all of this possible within 3 months and at one-third the cost originally estimated.

The Implementation

SuperAGI’s implementation of the RAG software for the pharmaceutical company involved several key phases to ensure seamless integration and significant improvements in data management. The project started with an initial assessment of the existing MySQL database system, identifying critical integration points and inefficiencies. Django was chosen for robust backend management, and NextJS was selected for the responsive frontend interface.

SuperCoder significantly streamlined the development of the RAG software by enabling precise and efficient implementation of several critical features:

  • Vector Database Integration:
    • Integration with MySQL Database: SuperCoder facilitated the integration with the existing MySQL database system.
    • Efficient Data Management: Automated the vectorization and cataloging of experimental data to ensure efficient data management and quick retrieval.
  • Open Source LLM Deployment:
    • On-Premise Processing: SuperCoder implemented an open-source LLM tailored to generate, retrieve, and augment data while ensuring all processing was securely conducted on the company’s private servers.
    • Security Compliance: Maintained strict security protocols to comply with industry standards and protect sensitive data.
  • Advanced Search Capabilities:
    • Complex Queries: Enhanced search functions allowed researchers to perform complex scientific queries.
    • Instant Augmented Information: Enabled instant access to augmented information, significantly improving research efficiency and decision-making processes.
  • User-Friendly Interface:
    • NextJS-Powered Frontend: Developed a responsive and seamless user interface using NextJS.
    • Accessibility: Provided an intuitive user experience for R&D and business management teams, ensuring easy accessibility and usability of the system.

This comprehensive approach significantly improved data retrieval speed, enhanced data organization, and maintained strict security standards, allowing the pharmaceutical company to streamline its research and development processes effectively.

Results

SuperAGI’s custom RAG solution, powered by SuperCoder, transformed the pharmaceutical company’s data management and research capabilities, delivering substantial business impact and operational efficiency.

  • Significant Cost Savings: Using SuperCoder, Project Cost was reduced from $1.5M to 400k$, $1.1M in total cost savings.
  • Reduced Project Duration: The project was completed in just three months, cutting the initial six-month timeline by half.
  • Robust Security Compliance: Maintained zero data breach incidents with stringent security protocols and on-premise LLM deployment, ensuring full compliance with industry standards.
  • Enhanced Data Retrieval Speed: Increased data retrieval speed and login process time by 50%, enhancing efficiency in accessing and analyzing research data, and making all R&D data easily accessible to researchers and data engineers.

Conclusion

The collaboration between SuperAGI and the mid-sized pharmaceutical company demonstrates how AI powered autonomous systems can be used in enhancing data management and operational efficiency in R&D-centric industries. The Vector RAG software not only streamlined processes but also provided a scalable model for future data management needs, ensuring long-term benefits and innovation acceleration.