RAG & Local AI
AI that runs on your own data
Retrieval pipelines, self-hosted LLMs and SLMs, and vector databases — so your AI stays private, affordable and under your control.
Three Building Blocks
RAG Pipelines
Document ingestion, smart chunking, embeddings, hybrid search and reranking — grounded answers with citations instead of hallucinations.
Local LLMs & SLMs
Self-hosted models (Llama, Mistral, Qwen, Phi) on your server or a single GPU — no data leaves the building, no per-token bill.
Vector Databases
Qdrant, Milvus, pgvector or Chroma — tuned indexes, metadata filtering and the right distance metric for fast semantic search at scale.
Why Self-Hosted
Data Privacy & GDPR
Sensitive documents, customer data and IP never leave your infrastructure — a clean answer for compliance and legal.
Predictable Cost
A fixed server instead of a usage meter. Heavy workloads that would be expensive on an API become basically free to run.
No Vendor Lock-In
Open weights and open-source tooling — swap models, hosts or providers without rewriting your stack.
Offline & On-Prem
Runs in an air-gapped network, on the factory floor or on a laptop — wherever the cloud isn't an option.
How I Build It
Scope
Data sources, privacy needs, hardware budget
Index
Chunking, embeddings, vector store, retrieval eval
Serve
Local model, prompt templates, guardrails, API
Evaluate
Answer quality, latency, cost — measured, not guessed
Tools I Work With
Use Cases
Private Knowledge Base
Contracts, wikis and manuals answered instantly — on-prem
Offline Assistant
An AI helper for networks with no internet access
Document Search
Semantic search across millions of internal pages
Cost-Capped AI Features
High-volume AI in your product without the API bill
Let's build your private AI
Knowledge base, offline assistant, semantic search — let's scope it.