Vladimir S.

I help software teams turn AI ideas into reliable products - from early R&D and technical direction to production delivery.

I work with companies introducing AI into an existing product, moving an LLM prototype into production, or changing engineering processes around AI-assisted development.

My work starts before implementation: clarifying where AI creates real leverage, mapping workflows and constraints, prioritising use cases, and testing the technical direction before a team commits serious budget. I help with AI strategy, R&D, technical due diligence, architecture, build-vs-buy decisions, and AI-enabled SDLC.

I build production AI systems, including LLM applications, RAG platforms, AI agents, self-hosted models, computer vision, and machine learning. The difficult work is rarely choosing a model. It is designing the system around it: company data, context engineering, integrations, evaluation, monitoring, latency, cost control, security, and predictable behaviour.

Core areas:
✅ AI agents and workflows - tool use, MCP integrations, memory/context management, human approval gates
✅ Production engineering - Python, FastAPI, Docker, APIs, AWS/Azure, scalable data infrastructure
✅ RAG and internal knowledge systems - hybrid retrieval, pgvector/Qdrant, reranking, evaluation
✅ AI reliability - observability, evals, regression testing, audit trails, governance
✅ Text-to-Voice(ASR), Voice-to-Text(TTS) and real time voice agents

Recent work includes a RAG troubleshooting system for a UK manufacturer funded by Innovate UK; an AI investment-research platform; LLM compliance automation for AdTech; and an AI-assisted music-therapy product used in dementia and autism care.

I have 10+ years of software engineering experience and stay personally involved from discovery and R&D through architecture and delivery. If you are assessing an AI opportunity or need to make an AI system reliable in production, I can help define the right next step.