Production-Grade AI Systems That Scale Beyond Prototypes
Designing reliable AI systems using LLMs, RAG pipelines, AI agents, workflow automation, and scalable infrastructure — engineered for the environments where reliability and cost matter.
Most AI projects fail in production due to architecture mistakes — not models.
Stateful workflows, validation loops, and observability by default.
Model routing, caching, and structured outputs that hold the bill down.
Engineering the full AI stack
From architecture to deployment — production AI systems that survive contact with real users, real data, and real budgets.
AI Architecture
Design scalable production systems from first principles.
LLM Applications
OpenAI, Claude, Llama, and bespoke LLM APIs in production.
RAG Systems
Knowledge retrieval, vector search, and grounded generation.
AI Agents
Autonomous workflows and stateful multi-agent systems.
Workflow Automation
Operational AI woven into real business processes.
Cloud Deployment
AWS, Azure, GCP, Docker — production infrastructure.
Production AI systems shipped end-to-end
A sample of recent engagements — multi-agent automation, document AI, and enterprise RAG.
Autonomous AI Agent System for Sales & Workflow Automation
Multi-agent system that autonomously researches, enriches, scores, and engages B2B leads.
AI Document Processing & Intelligent Data Extraction Platform
Production document AI for logistics: OCR + LLM interpretation + structured extraction at scale.
Enterprise AI Assistant for Internal Knowledge Search
Secure internal RAG assistant unifying Notion, Drive, and support systems into one semantic layer.
The stack behind the systems
Battle-tested tools, used with intent — never just because they're trendy.
How a system actually gets built
A six-stage engagement model that puts architecture before code and evaluation before launch.
Discovery
Map business goals, constraints, and where AI actually creates value.
Architecture
Design the system end-to-end before writing production code.
Implementation
Build with disciplined engineering, not glue code.
Validation
Evaluation harnesses, golden sets, and continuous quality checks.
Deployment
Ship with observability, rollback paths, and cost controls.
Optimization
Iterate on quality, latency, and cost from real production data.
Field notes from production AI
Practical perspectives on agents, RAG, LLM engineering, and the architecture decisions that make or break systems.
Building Enterprise AI: A Guide to Governance and Compliance
Governance is not a checklist you fill in after launch. It is the operating system that keeps production RAG and agent systems safe, auditable, and cost-controlled at scale.
Why Most AI Projects Break After the Prototype Stage
The demo works. The pilot ships. Six weeks later, the system is unreliable, expensive, and nobody trusts the output. Here is what actually goes wrong — and the maturity checklist that prevents it.
Have an AI system that needs to graduate from prototype to production?
I work with technical and operational teams to design, build, and de-risk LLM, RAG, and agent systems that need to behave reliably in the real world.
- Architecture review & system design
- Multi-agent and RAG implementations
- Production readiness, eval & cost optimization
- Fractional AI architect engagements