The Problem: RFPs Are a Time Sink
If you've ever worked in enterprise sales or solution consulting, you know the pain of responding to Request for Proposals (RFPs). These documents can contain hundreds of questions about your product's capabilities, security posture, compliance certifications, and integration features.
A typical scenario: A solution consultant receives a 200-question RFP questionnaire on a Thursday afternoon with a Monday deadline. They spend the entire weekend digging through product documentation, data sheets, and previous RFP responses, trying to craft accurate answers while maintaining consistency with what the company has promised before.
We knew there had to be a better way. With the explosion of Large Language Models (LLMs) and the maturation of Retrieval-Augmented Generation (RAG) techniques, we saw an opportunity to build something that could transform how our team handles RFPs.
The result is RFP Aviator - an AI-powered system that processes RFP questionnaires, retrieves relevant product documentation, generates accurate responses, and produces a compliance matrix. In testing, we achieved 90% accuracy and can process 100 questions in just 10 minutes.
Why RAG Instead of Pure LLMs?
When we started this project in 2024, ChatGPT and Claude were already impressive. Why not just paste questions into an LLM and call it a day?
The Hallucination Problem
Pure LLMs, no matter how advanced, have a fundamental limitation: they generate responses based on patterns learned during training, not real-time facts. When asked "Does your product support SAML 2.0 authentication?", a vanilla LLM might confidently say "Yes" - even if your product doesn't support it.
For RFP responses, hallucinations aren't just embarrassing; they're potentially contract-breaking. A single incorrect claim about compliance or capability could lead to legal issues down the road.
The Knowledge Cutoff Problem
LLMs are trained on data up to a certain date. Product features change constantly. New versions ship. Capabilities are added or deprecated. An LLM trained on 2023 data won't know about features released in 2024.
Enter RAG: Grounding LLMs in Your Data
Retrieval-Augmented Generation solves both problems by:
- Retrieving relevant chunks of your actual documentation before generation
- Augmenting the LLM prompt with this retrieved context
- Generating responses grounded in real, up-to-date information
The LLM becomes a sophisticated language engine that synthesizes information from your knowledge base rather than relying on potentially outdated or incorrect training data.
Our Technology Choices
Python + LangChain: The Orchestration Layer
When we evaluated frameworks for building our RAG pipeline, LangChain stood out for composability, provider flexibility, and battle-tested integrations.
ChromaDB: Lightweight But Capable
For our vector database, we chose ChromaDB for its self-hosted nature, simplicity, and Python-native feel. For an enterprise tool with tens of thousands of document chunks across ~20 products, ChromaDB is perfect.
Llama 3.1 70B via AWS Bedrock
We went with Meta's Llama 3.1 70B via AWS Bedrock because:
Data Sovereignty: Customer data never leaves our AWS environment
No GPU Management: Bedrock handles scaling and availability
Cost Predictable: Pay-per-token, no idle GPU costs
Building the Multi-Product RAG Pipeline
One unique aspect of RFP Aviator is that we support 20+ different products, each with its own documentation corpus and domain expertise requirements.
Product-Specific Vector Stores
Instead of one giant vector store, we maintain separate ChromaDB instances per product: Application Security, Identity Management, SIEM, Data Security, and 15+ more.
Role-Based Prompt Engineering
Different products require different expertise personas. We define roles like "Application Security architect, " or "Identity and access management architect, ".
Compliance Scoring
Our system automatically classifies responses into "Compliant", "Non-Compliant", or "Review required" based on Yes/No prefixes, then aggregates to a compliance percentage.
Real-World Performance
After deploying RFP Aviator internally, we measured impressive results:
- 90% Accuracy — Validated against expert responses
- 100 questions in ~10 minutes — Processing speed that transforms deadlines
- 70% reduction in consultant hours per RFP
- Zero contradictions — Eliminated inconsistent responses across RFPs
The Value Proposition
For Solution Consultants
Time Reclaimed: Hours of documentation diving replaced by minutes of response review
Consistency: No more contradicting what a colleague said in last month's RFP
For Sales Teams
Faster Turnaround: Meet aggressive RFP deadlines without weekend work
Higher Win Rates: More time for strategic positioning
For the Organization
Scalability: Handle more RFPs without proportionally increasing headcount
Knowledge Preservation: Institutional knowledge captured in vector stores
Conclusion
RFP Aviator taught us that successful enterprise AI isn't about chasing the most powerful model. It's about:
Solving a real pain point: RFP responses were genuinely painful 2.
Grounding in truth: RAG prevents hallucination through retrieval 3.
Measuring what matters: 90% accuracy, 10-minute processing 4.
Building for humans: AI drafts, humans review - not full automation
The AI landscape will continue advancing. But the fundamentals - retrieval augmentation, prompt engineering, human-in-the-loop workflows - these patterns will persist.