Get the details about the creation of WWT's RFP Assistant, an innovative GenAI tool designed to streamline the proposal response process. This article delves into the complexities and meticulous steps involved in developing a tool that not only enhances efficiency but also aligns with WWT's commitment to delivering meaningful, outcome-focused solutions.

Why we developed RFP Assistant

Complex proposal processes: With around 800 RFPs received annually, WWT's Proposal team faces the challenge of qualifying, pricing, and responding to diverse and intricate requests.
Skill integration: Responding to RFPs requires a blend of technical, business, and strategic skills, making it a prime candidate for AI augmentation.
Efficiency goals: Our aim was to build a tool that could significantly reduce the time and effort required for proposal managers to generate high-quality responses.

How we built it

GenAI Techniques: Leveraging large language models (LLMs) and retrieval-augmented generation (RAG), we created features like the RFP Summarizer, RFP Chatbot, and Response Generator.
User-centric design: The tool was designed with core principles in mind—human-first, asynchronous processing, and controllability—to ensure it serves as an effective companion to proposal managers.
Iterative development: Continuous refinement based on user feedback has been crucial in enhancing the tool's functionality and user experience.

Pooja Patel

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