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Research-grade Self-Correcting RAG agent built with LangGraph that retrieves knowledge, generates answers, evaluates grounding/relevance/completeness, and iteratively self-improves with confidence scoring and memory.
Backend document processing pipeline using n8n and Gemini AI. Receives files via webhook, extracts structured data, calculates confidence scores and stores results in Supabase and Google Sheets.
A modular AI-driven pipeline for cleaning, normalizing, and standardizing large-scale inventory data with automated SKU generation, confidence scoring, and human-in-the-loop validation.
Governed knowledge ingestion engine with artifact lineage, confidence evolution, replayable stages, and append-only persistence. Transcript is just the input — output is traceable artifacts.