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Artificial Intelligence

AMOLED AI: Air-Gapped Private RAG & Document Intelligence

Enterprise retrieval-augmented generation (RAG) and document verification architecture engineered with FastAPI, semantic chunking, and PostgreSQL pgVector embeddings for zero-leakage knowledge retrieval.

100% On-Premise Data Sovereignty & Sub-Second Query Synthesis

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PythonFastAPIPostgreSQL pgVectorSQLAlchemyLangChainPrivate LLM

AMOLED AI: Air-Gapped Private RAG & Document Intelligence

Executive Overview

AMOLED AI is an enterprise-grade private document intelligence and retrieval-augmented generation (RAG) platform engineered for strict on-premise environments, academic verification, and high-security enterprise knowledge bases where data confidentiality is paramount.

The Core Problem & Architectural Bottlenecks

  • The Enterprise AI Privacy Dilemma: Enterprises, financial institutions, and academic research bodies possess thousands of confidential contracts, policy documents, and proprietary research papers. Routing private enterprise documents through public commercial LLM APIs risks proprietary trade secrets and violates sovereign data residency laws.
  • Hallucination & Lack of Verifiable Citations: Commercial cloud models routinely generate convincing hallucinations without verifiable source provenance, rendering them dangerous for legal compliance, technical audits, and academic verification where every statement must be anchored to exact source coordinates.
  • Gigabyte-Scale Search Latency: Traditional keyword search engines fail to understand domain-specific semantic context, forcing compliance and engineering teams to spend hundreds of hours manually cross-referencing multi-hundred-page PDF manuals.
  • Complex Multi-Format Ingestion: Technical documentation is locked inside complex PDFs with tables, formulas, and diagrams that standard OCR and basic text splitters fail to parse correctly.

The Engineering Solution

  • Air-Gapped Vector Retrieval: PostgreSQL with native pgVector extension for fast on-premise semantic similarity searches without external vector cloud dependencies.
  • FastAPI Asynchronous Ingestion: Multi-format document parser (PDF, DOCX, TXT) with recursive character text chunking and metadata attribution.
  • Private LLM Inference Engine: Local model integration (Llama 3 / Mistral) producing citation-backed syntheses with zero external data transmission.
  • Academic & Policy Verification Module: Automated source verification cross-referencing claims directly against authorized internal documents.

Measurable Business Impact & ROI

  • 100% on-premise data sovereignty with zero external network leakage.
  • < 450ms semantic search and document retrieval latency across gigabyte-scale document indexes.
  • Citation-backed generative responses linking directly to exact source page coordinates.
  • 10x faster institutional research synthesis for compliance and technical audit teams.

Technology Stack

  • AI Backend: Python 3.11 / FastAPI / SQLAlchemy / Pydantic
  • Vector & Data: PostgreSQL + pgVector / SQLite Local Cache
  • NLP / Embeddings: HuggingFace Local Embeddings / LangChain
  • Frontend Interface: React / Vite / Modern Document Explorer UI

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