Pitch Deck Analysis and AI Chatbot

Venture capitalists and startup teams who review many pitch decks. This workflow processes PDF pitch decks from Airtable. It uses AI to read pages, extract key startup info, update the database, and build a searchable knowledge base. It also creates a chatbot for asking questions about the decks. Duplicate the sample Airtable base. Set up credentials for Airtable, OpenAI, and Qdrant. Point nodes to your base and collection IDs. Activate the Airtable trigger or use manual test. n8n account. OpenAI API key. Qdrant instance (cloud or self-hosted). Airtable personal access token. Optional: Self-host Stirling PDF for privacy. Change AI prompts for different VC personas. Add or remove extraction fields. Switch vector store or LLM. Extend chatbot tools for more actions. Key Technologies: n8n,Airtable,OpenAI GPT-4o,Qdrant Vector Store,Stirling PDF Automation Value: Automates manual pitch deck reading and data entry, saving hours per deck. Extracts structured insights with AI, reducing errors from OCR. Enables team-wide Q&A on decks via chatbot, speeding up due diligence. Scales to handle hundreds of decks without extra staff. Flags facts to check and judges VC fit, aiding investment decisions. Best Practices: Self-host Stirling PDF and Qdrant for data privacy. Test extractions on sample decks to refine schema. Monitor OpenAI costs; resize images further if needed. Use versioning in Airtable for audit trails. Add error handling nodes for failed API calls. Customize VC persona prompt for your firm's focus.