Custom ChatGPT-Style AI to manage RFP Workflows

Business Process Management

Machine Learning


Collaborative RFP Workflow Management Platform powered with Transformer AI models for faster and quicker sales turnaround.

Rest assured, we have a strict no-spam policy.
Your inbox is safe with us!

Protected By reCAPTCHA
AI Consulting Services and Advanced Data Analytics
Rated 5-stars by Satisfied customers
Scroll Down for More Details

The project required the development of a sophisticated RFP Management Software to enhance business process management in handling Requests for Proposals. The software was envisaged as a comprehensive platform that not only manages RFP processes from beginning to end but also offers significant time-saving benefits by automating aspects of the proposal development process. This was to be achieved through the integration of AI and large language models to automate the Q&A process, thus significantly reducing bid time.

Crucially, the platform was to be equipped with robust features for managing sensitive data, ensuring the utmost security and compliance with data protection regulations.

In summary, the key business requirements were:

  • Business Process Management: The software should enhance the management of business processes related to RFPs, providing an end-to-end solution.
  • Time-Saving AI Automation: Leveraging AI and large language models, the software should automate the Q&A process, significantly reducing bid time, and therefore saving proposal time.
  • Sensitive Data Management: The platform should ensure secure handling and protection of sensitive data in compliance with data protection regulations.
  • User-Friendly Collaboration: The software should provide a user-friendly environment that promotes real-time collaboration, further streamlining the proposal development process.


  • Enhanced Information Search & Retrieval: Upgrade the software’s ability to autonomously search, retrieve, and generate matching information, thereby optimizing the proposal creation process.
  • Intelligent Document Parsing: Design a system capable of discerning and extracting specific sections or subsections from complex documents, improving the efficiency of the proposal generation process.
  • Automated Response Generation: Develop sophisticated AI algorithms that can suggest accurate responses to extracted questions, saving time and enhancing the quality of the proposal.
  • Secure Management of Sensitive Data: Implement robust measures to securely manage sensitive data, ensuring compliance with data protection regulations and maintaining user trust.
  • Collaborative Multi-User Platform: The software provides varied user roles with distinct permissions and privileges, facilitating better collaboration and management of the RFP process.
  • AI-Powered Automation: The platform employs AI to automate the RFP process, enabling seamless and swift responses to RFPs.
  • Intelligent Document Analysis: The software auto-analyses documents to understand and extract requirements using advanced NLP and ML techniques.
  • Customizable Auto-Responses: The platform automatically answers a multitude of questions and allows users to customize their responses to each requirement.
  • Knowledge Management: The software features a Knowledge Manager that continuously curates, learns, and shares new knowledge, creating a crowd-sourced knowledge base accessible to all employees.
  • Expert Connection Capabilities: Additional features facilitate connection among colleagues for specialist response processes, assignment of content tasks to SMEs, and easy tracking of progress.
  • Dynamic Document Generator: The platform includes a dynamic document generator that allows users to create draft proposals with auto-formatting and smart organization capabilities.
  • Bulk Import Feature: A bulk import feature is integrated into the software, enabling the import of past RFP documents to speed up the response process.
  • Advanced ML and LLM Models: The platform utilizes a Machine Learning model (Random Forest Classifier) and the state-of-the-art Large Language Model (Dolly 2) for section identification, question extraction, and answer recommendation.
  • Open Source and On-Premise Deployment: The platform is based on commercially usable open-source technologies and can be deployed on-premise, offering a cost-effective solution for organizations.
  • Robust and Scalable Architecture: Built using the Dropwizard Java framework and Python for LLM models, with containerization achieved using Docker, the platform is robust, scalable, and easily maintainable.

The implementation of the RFP Management Software has proven to be immensely beneficial for several organizations, offering significant savings in time, cost, and resources.

  • Time Savings: The AI-powered automation feature has been a game-changer in reducing the time taken to respond to RFPs. The intelligent document analysis and auto-response features have enabled organizations to respond to RFPs in a fraction of the time it would take manually. Moreover, the bulk import feature has expedited the process by leveraging past RFP documents, further saving time.
  • Reduced Team Size: The collaborative multi-user platform, combined with expert connection capabilities, has optimized team collaboration and reduced the need for large proposal teams. The platform’s ability to assign content tasks to Subject Matter Experts (SMEs) and match experts to topics has streamlined team workflows, requiring fewer personnel to manage the RFP process.
  • Cost Efficiency: The software has offered cost benefits on multiple fronts. The reduction in time spent on the RFP process translates to substantial cost savings. Fewer personnel required to manage the RFP process means lower overhead costs. Furthermore, being a commercially usable open-source software that can be deployed on-premise, the platform eliminates the need for expensive licenses and ongoing subscription fees.
  • Faster Turnaround: The software’s AI-driven automation and streamlined workflow have significantly accelerated the RFP response process. The time saved from automatic question answering, intelligent document analysis, and streamlined collaboration allows for much faster turnaround times on proposals. This increased speed not only helps organizations meet tight deadlines but also provides additional time for review and refinement, thereby improving the quality of the proposals submitted. Consequently, organizations are better positioned to win bids, contributing to higher success rates and increased revenue.
  • JAVA
  • DropWizard
  • Python
  • Flask
  • Docker
  • LLM
  • Transformer Models
  • Dolly 2
  • Langchain
Dexlock Portfolio Dexlock Portfolio Dexlock Portfolio
Dexlock Technology
Dexlock Technology
Dexlock Technology
Dexlock Technology
Dexlock Technology
Dexlock Technology

More Projects

Category :