Most AI agents break in production for one simple reason: Teams dump raw OCR and unverified context directly into an LLM, hoping the prompt will magically prevent hallucinations.

It doesn’t. You get garbled tables, missed math, and fabricated citations.

To solve this, I built a zero-hallucination document intelligence engine using a deterministic 4-tool pre-processing pipeline before any data reaches the model.

Here’s the architecture: 1️⃣ Deterministic Pre-processing: Extract and segment complex tables, formulas, and citations first. 2️⃣ Strict JSON Schema Guards: Enforce ground-truth Pydantic schemas so downstream APIs never crash. 3️⃣ Grounded Reasoning: Feed verified structure into Google Gemini Flash for sub-second streaming responses.

The result? Zero fabricated citations and clean, structured output every single run.

🌐 You can test the live demo here: https://agent-pdf-summary.vercel.app

━━━━━━━━━━━━━━━━━━━━━━━━━━

A quick update on my end: As a Verified GitHub Sponsor and open-source contributor (including to the viral Omi AI Wearable ecosystem), I am officially opening 1–2 client slots this month for freelance / contract work.

What I help startups and engineering teams build: • Autonomous Multi-Agent Workflows (LangGraph, CrewAI, Python) • Production RAG & Document Intelligence (Zero-hallucination pipelines) • Full-Stack AI MVPs shipped in 2–3 weeks (FastAPI + React/Next.js) • LLM Evaluation Suites & Self-Healing Feedback Loops

If you’re looking to offload an AI workflow or take an AI product from prototype to production:

Drop me a DM here or connect on GitHub: github.com/aiwithrajan

Let’s build something reliable. 🚀

#AI #MachineLearning #OpenSource #LangGraph #Python #BuildInPublic #SoftwareEngineering