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I will build a custom vector database and local rag engine
Maxim
Over deze dienst
Are you paying high monthly fees for cloud vector databases like Pinecone while risking data privacy?
I architect custom, ultra-fast vector search engines and fully air-gapped RAG pipelines for on-premise environments.
What I deliver:
Embedded Vector Engines: High-performance C99 core integrated with SQLite WAL mode.
Sub-millisecond Latency: Optimized similarity search with sub-ms query times without heavy cluster overhead.
100% Data Sovereignty: Zero cloud leakage. Embeddings, documents, and weights stay strictly on your local hardware.
Hybrid Retrieval: Vector similarity coupled with BM25/FTS5 full-text search for high recall and precision.
Seamless Integration: Clean Python APIs and microservices ready for production.
Stack:
C99, SQLite (WAL mode, custom vector extensions), Python, Local LLMs (Llama, DeepSeek), Linux.
Whether you need a PoC audit, custom engine integration, or an end-to-end air-gapped RAG, I build systems engineered for raw speed and data security.
Please contact me prior to ordering to discuss technical scope and hardware requirements.
Maak kennis met Maxim
Maxim
AI Systems Architect and High Performance SQLite Optimizer
- Afkomstig uitOekraïne
- Lid sindsfeb 2026
Talen
Russisch, Engels
Systems Architect and AI Engineer specializing in low-latency, air-gapped data pipelines and custom vector search engines.
I help companies eliminate high cloud costs (Pinecone, Qdrant Cloud) and meet strict GDPR/HIPAA standards with high-performance embedded infrastructure.
What I deliver:
- In-process vector engines with SQLite WAL mode
- Sub-millisecond query paths (0.42 ms p50)
- Air-gapped, privacy-first local RAG systems
- Autonomous AI agent architectures with persistent memory
Fast delivery, clean code, zero cloud lock-in. Let us optimize your AI!
Mijn portfolio
Veelgestelde vragen
Why use SQLite + C over cloud vector DBs?
It eliminates recurring SaaS costs, ensures 100% data sovereignty with zero external leaks, and delivers sub-millisecond query latency directly on local hardware without distributed cluster overhead.
Can this system operate completely offline?
Yes. The entire architecture runs in 100% air-gapped, sovereign environments using local embeddings and local LLMs (like Llama or DeepSeek) without any internet connection.
What do you need from me to get started?
Your target use case, vector dimensions, data volume, hardware specifications, and latency targets. A benchmark dataset or sample schemas are also helpful.
What latency and throughput can I expect?
With optimized C99 routines and SQLite WAL, typical similarity search latencies stay below 1ms per query on standard CPU hardware without GPU acceleration.
Do you sign an NDA, and who owns the IP?
Yes, I strictly sign mutual NDAs before reviewing sensitive code or data. 100% of the developed source code and IP belong entirely to you upon delivery.
