6-day longest streak
Hi there, I'm Stefan 👋 > "Most RAG projects don't fail because of the LLM. They fail because they treat PDF ingestion as a simple file upload. They hallucinate because…
Hi there, I'm Stefan 👋
> *"Most RAG projects don't fail because of the LLM. They fail because they treat PDF ingestion as a simple file upload. They hallucinate because they guess instead of verify."*
I am an AI-Native Architect focused on the Ingestion Gap and Verifiable Truth. My mission is to replace "Digital Paper" (dead PDFs) with structured, semantic knowledge that allows Local AI to reason without hallucinations.
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🏛️ Flagship Mission: PantheonRAG
The Consensus Engine for Mission-Critical Data.Pantheon is not a chatbot. It is a scientific instrument designed to eliminate hallucinations through rigorous multi-agent debate and graph-based verification.
- ⚖️ Solomon Consensus Engine: Agents (Legal, OCR, Vision) must reach agreement before answering.
- 🔬 The Laboratory: A "Glasshouse" for radical transparency and auditability.
- 🧬 Surgical HITL: Precision tools for expert intervention in the reasoning chain.
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🚀 The Ecosystem
I build modular, production-ready kits to fix the "Garbage In" problem for high-compliance environments (Public Sector / Enterprise).🏗️ Architecture & Platforms
The Blueprint for BSI-compliant, self-hosted RAG. *Features*: Ingestion Triage, GraphRAG, Semantic Caching, and Full Observability. *Status*: Architecture Preview / Closed Source Engine.🛠️ Essential Tooling
The proof that RAG can handle complex tables if you use Docling + Vision Validation. *Status*: Open Source Audit Tool. Production-grade document ingestion pipeline using Docling v2. *Solves*: Layout Analysis, Table Reconstruction, Markdown Conversion.🤖 Proven in Production
A fully autonomous, privacy-first AI email assistant running locally The proof that my ingestion engine works in the wild DSVGO / CCPA compliant---
🧠 The "Ingestion-First" Stack
I don't believe in "One Model Fits All". I believe in Triage and Tiers.| Ingestion | Intelligence | Memory | Observability |
| :--- | :--- | :--- | :--- |
| Docling v2 | Qwen2-VL | Neo4j | LangGraph |
| PyMuPDF | Ollama | ChromaDB | Sentry |
| Marker | DeepSeek | Redis | Grafana |
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🌱 Philosophy
- Structure > Vectors: Embeddings are useless if the input table was ripped apart
- Verification > Generation: Don't just generate text. Verify it against the source
- Local > Cloud: Data sovereignty (GDPR/BSI) is not optional. I build for air-gapped reality
- Logic > Magic: I prefer deterministic code for business rules over probabilistic LLM guessing
📫 Connect & Context
- Reddit: u/ChapterEquivalent188 - Discussing the "PoC Trap" & Ingestion Realities.
- Focus: Currently open for strategic dialogue regarding High-Compliance RAG Architectures (Public Sector / Industry).
- 2dogasandanerd - gmail.com My Agnets told me you said Hi
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ClawRag ★ PINNED
RAG system combining Docling document processing with ChromaDB vector storage to power openclaw
Python ★ 152 4mo agoExplain → -
Knowledge-Base-Self-Hosting-Kit ★ PINNED
A Docker-powered RAG system that understands the difference between code and prose. Ingest your codebase and documentation, then query them with full privacy and zero configuration.
Python ★ 351 4mo agoExplain → -
DAUT ★ PINNED ▣
DAUT – Documentation Auto Updater - AI-powered documentation generator for your codebase. MCP-Connector
Python ★ 6 4mo agoExplain → -
smart-ingest-kit ▣
Stop using static chunk sizes. A lightweight, production-ready RAG ingestion toolkit. Uses Docling for layout-aware parsing and applies smart heuristics for optimal chunking (PDF vs Code vs MD). Extracted from a production RAG platform
Python ★ 71 4mo agoExplain → -
RAG_enterprise_core
Enterprise-grade Retrieval-Augmented Generation system with microservices architecture.
★ 23 4mo agoExplain → -
validated-table-extractor ▣
PDF table extraction tool
Python ★ 13 4mo agoExplain → -
Liability-Trap---Semantic-Twins-Dataset-for-RAG-Testing
Liability Trap - Semantic Twins Dataset for RAG Testing
★ 6 6mo agoExplain → -
smart-router-kit
This kit demonstrates how to implement an **"Ingestion Traffic Controller"** for RAG systems. Instead of blindly chunking and embedding every document, we use a small LLM pass to route documents to the correct semantic collection and choose the optimal processing strategy.
Python ★ 4 7mo agoExplain → -
rag_pdf_audit
Tool to compare pdf extraction methods
Python ★ 3 7mo agoExplain → -
2dogsandanerd
No description.
★ 2 4mo agoExplain → -
TrustRAG ⑂
Code for "TrustRAG: Enhancing Robustness and Trustworthiness in RAG" AAAI 2026 Workshop on Trust and Control in Agentic AI (TrustAgent)
★ 0 1y agoExplain → -
2dogsandanerd.github.io
No description.
HTML ★ 0 4mo agoExplain → -
rag_auditor
No description.
★ 0 5mo agoExplain →
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