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AI News Digest Automation with Human-in-the-Loop Review (RSS → Social Publishing)
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Built a full-stack AI automation system that transforms how the company responds to RFPs.
Users upload RFP documents (PDF/Excel) through a web portal. An n8n pipeline processes the upload: AI parses the document to extract key fields (issuer, scope, budget, deadline), classifies the service line, matches content from an Airtable library (team bios, references, portfolio), and generates 80-90% complete proposal drafts with cover letter, company overview, technical approach, and more. Output is a formatted Word document ready for review.
Results:
✅ Reduced proposal creation from 4-6 hours to under 30 minutes
✅ 90% auto-complete rate on proposal sections
✅ 14+ RFPs processed through the pipeline
✅ Full proposal dashboard with status tracking
Tech: n8n, OpenAI, Airtable, Vercel, Webhooks
This is a really interesting automation workflow, Peter. Using AI to parse RFPs and streamline proposals is a smart use case.
AI-Powered Outreach Automation (n8n)
Built a workflow that turns outreach into a structured pipeline.
The system:
• Pulls verified leads from Supabase
• Uses AI to generate personalized emails
• Sends outreach via Gmail
• Monitors replies and analyzes sentiment
• Routes responses automatically (book meeting, review, or archive)
• Sends follow-ups if there’s no reply after 48 hours
Result: a fully automated pipeline that moves leads → conversations → booked meetings.
Designed for creators, agencies, and teams running outbound outreach at scale.
Nice workflow. Using AI to personalize outreach and track replies in one pipeline is really useful.
Built a Multimodal RAG system live on stream. From zero to working CLI in one session.
The goal: a terminal-based RAG pipeline using Haystack, Gemini Multimodal Embeddings, and Flash Lite, querying complex research PDFs through a conversational loop.
Three real engineering lessons from the build:
Chunking isn't optional. Raw PDFs exhaust token limits instantly. 6-page splits solved it.
InMemoryDocumentStore works for prototyping, but re-vectorizes on every launch. Persistent DBs like Weaviate are the next step.
Haystack's pipeline is strict. Mis-wiring a retriever to a generator crashes the loop immediately. API contracts matter.
Result: a CLI that maps user queries to the top 4 semantic chunks in RAM and returns grounded, non-hallucinated answers from your own documents.
Every mistake, every fix, on camera.
"Ugurcan Uzunkaya this is seriously impressive! 🔥 Building a full RAG pipeline live on stream in one session is no joke!
That chunking lesson is so real — token limits hit different when you're working with raw PDFs.
And the Haystack pipeline point about API contracts is...
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