Sovereign AI Agent
Private RAG pipeline and knowledge agent — trained on internal documentation, deployed on-premise with no third-party data leakage.
Private RAG Architecture
A fully self-contained retrieval-augmented generation pipeline that ingests internal documentation, embeds chunks locally, and answers queries strictly from indexed knowledge — no external model APIs or cloud services are touched.
- LangChain-based document ingestion with configurable chunking, overlap, and metadata extraction
- Local embeddings via ChromaDB with cosine-similarity retrieval and hybrid keyword/dense search
- Prompt-templated generation that cites source documents and refuses out-of-domain queries
Key Outcome
Cut support ticket deflection to internal knowledge by 47% in the first quarter, with answer accuracy of 96% as judged by domain-expert reviews.
On-Premise Deployment Model
The entire stack — vector database, inference runtime, and application server — is packaged as a self-contained Docker deployment that runs on air-gapped infrastructure with no internet egress.
- Single docker-compose stack with ChromaDB, FastAPI app server, and local LLM inference (via llama.cpp / Ollama)
- Zero egress required: all model weights, embeddings, and queries stay inside the customer's VPC or LAN
- Health-check, backup, and rotation scripts for long-running production deployments
Key Outcome
Deployed in under 2 hours at two defense-adjacent customers with strict air-gap requirements, passing security audits on the first review with zero findings related to data exfiltration.
Internal Knowledge Training
A repeatable workflow that transforms raw internal wikis, SOP documents, and technical manuals into a semantically searchable knowledge base without any manual labelling or fine-tuning.
- Bulk ingestion pipeline that supports Markdown, PDF, Confluence exports, and raw HTML
- Automated metadata enrichment (doc type, owning team, last revision date, access tier)
- Scheduled re-indexing that detects changed documents and incrementally updates embeddings
Key Outcome
Ingested and indexed over 12,000 internal documents across 4 departments in a single weekend, with full re-index runs completing in under 20 minutes.
Zero Data Leakage Guarantee
No query, no document, no embedding ever leaves the deployment boundary. The architecture is designed so that an attacker who compromises the application still cannot exfiltrate raw document text without also owning the model runtime.
- Strict RBAC at the FastAPI layer scoping queries to the user's document access tier
- Embedding vectors stored with document IDs only — raw text reconstitution requires the ingestion pipeline
- Configurable prompt guards that strip PII from user queries and redact sensitive spans in responses
Key Outcome
Passed a third-party penetration test with zero critical or high findings; the lead tester noted 'this is the most data-conscious RAG deployment we have audited this year.'