You could build an AI-powered RFP response assistant using a combination of document automation, retrieval-augmented generation (RAG), and a structured workflow.
I'm thinking something like:
1. Extract and Parse the RFP
Use an LLM for summarization and section extraction.
Leverage OCR tools (e.g., Tesseract, AWS Textract, or Azure Form Recognizer) if the RFPs are PDFs with scanned text.
Convert the RFP into structured data using LangChain’s document loaders or Unstructured.io.
2. Automate Reusable Content Insertion
Maintain an answer bank (e.g., .txt, .md, Notion, Airtable, Google Docs, or even a vector database like Weaviate or Pinecone).
Implement retrieval-augmented generation (RAG) to pull relevant answers dynamically.
LlamaIndex or LangChain can help fetch the closest responses.
Use Embeddings (OpenAI, Cohere, or Ada models) for semantic search.
If a response needs tweaking, allow the AI to highlight differences and suggest edits.
3. Prompt for Missing Information
If the AI detects missing information:
Generate a draft response using context from existing data.
Provide a structured form/UI (e.g., Google Forms, Typeform, or an internal dashboard) for manual input.
4. Generate the Final RFP
Use LLMs to rewrite responses for coherence and consistency.
Automate formatting with Markdown-to-PDF tools (Pandoc, LaTeX) or Microsoft Word templates.
Consider using Zapier or Make.com to integrate responses into a shared document repository.
Low-Code/No-Code Approach
If you want minimal coding:
Microsoft Copilot (Word + Excel) or Google Gemini AI could automate drafting and inserting responses.
Zapier + OpenAI + Google Docs can build a pipeline to parse, retrieve, and generate answers.
Notion AI can assist in structured knowledge retrieval.
Would love to hear more about your constraints (e.g., compliance needs, integration with existing tools) to refine this further.
Thanks for the response here -- 0 constraints. Lots of PDFs in and a final PDF out. Questions vary per RFP but we have 90% of cases covered in a google doc answer bank. I'd be curious how this could be within a template -- so maybe Copilot would be the right tool here. Raw output of text to then format would still be 20% of the work without that.
I'm thinking something like:
1. Extract and Parse the RFP
Use an LLM for summarization and section extraction.
Leverage OCR tools (e.g., Tesseract, AWS Textract, or Azure Form Recognizer) if the RFPs are PDFs with scanned text.
Convert the RFP into structured data using LangChain’s document loaders or Unstructured.io.
2. Automate Reusable Content Insertion
Maintain an answer bank (e.g., .txt, .md, Notion, Airtable, Google Docs, or even a vector database like Weaviate or Pinecone).
Implement retrieval-augmented generation (RAG) to pull relevant answers dynamically.
LlamaIndex or LangChain can help fetch the closest responses.
Use Embeddings (OpenAI, Cohere, or Ada models) for semantic search.
If a response needs tweaking, allow the AI to highlight differences and suggest edits.
3. Prompt for Missing Information
If the AI detects missing information:
Generate a draft response using context from existing data.
Provide a structured form/UI (e.g., Google Forms, Typeform, or an internal dashboard) for manual input.
4. Generate the Final RFP
Use LLMs to rewrite responses for coherence and consistency.
Automate formatting with Markdown-to-PDF tools (Pandoc, LaTeX) or Microsoft Word templates.
Consider using Zapier or Make.com to integrate responses into a shared document repository. Low-Code/No-Code Approach
If you want minimal coding:
Microsoft Copilot (Word + Excel) or Google Gemini AI could automate drafting and inserting responses.
Zapier + OpenAI + Google Docs can build a pipeline to parse, retrieve, and generate answers. Notion AI can assist in structured knowledge retrieval.
Would love to hear more about your constraints (e.g., compliance needs, integration with existing tools) to refine this further.