AI-native infrastructure,
production-ready, in weeks.
I've built multi-tenant AI platforms from scratch — RAG pipelines, agentic workflows, cost-controlled LLM orchestration, vector search, Stripe billing.
I know where the expensive mistakes are before you make them. Across fintech, AI SaaS, industrial supply, and cybersecurity — I've shipped it and it's running in production.
What I Do
AI Architecture Design
Define the right stack before you build it. RAG vs. fine-tuning, vector DB selection, multi-tenancy design, cost controls, observability. Avoid the expensive mistakes.
- LLM stack selection (model, embedding, vector DB)
- Multi-tenant AI architecture with data isolation
- Cost modeling and usage-based billing design
- Observability and evaluation framework
RAG & Agentic Systems
Production-grade retrieval-augmented generation and agentic workflow engineering. Not tutorial-level — real pipelines with namespacing, re-ranking, and function calling.
- Namespace-isolated vector search per tenant
- LangGraph / LangChain agentic workflow design
- Function calling and tool use orchestration
- Evaluation pipelines and hallucination monitoring
Full-Stack AI Product Build
End-to-end product delivery. Auth, database schema, AI layer, payments, deployment. I own the full stack so there are no handoff gaps between AI and product.
- Next.js + FastAPI production architecture
- Supabase / PostgreSQL with RLS
- Stripe integration and credit-based billing
- CI/CD on Fly.io, GCP, or AWS
Technical Hiring & Team Structure
Define the right roles for an AI-native engineering team, run the technical hiring process, and establish code review and engineering culture from day one.
- AI/ML engineer role definition and sourcing
- Technical interview design
- Engineering process and code review culture
- Tech lead identification and mentorship
Infrastructure & Cost Optimization
LLM API costs get expensive fast. I design systems with cost as a first-class constraint — caching, batching, prompt compression, model routing.
- LLM cost modeling and token optimization
- Caching strategies (semantic, exact-match)
- Model routing (expensive ↔ cheap by query type)
- Cloud resource right-sizing
Technical Due Diligence
Pre-investment or pre-acquisition technical assessment of AI-native companies. Architecture review, team capability, scalability risk, and honest cost projections.
- AI architecture and codebase review
- LLM infrastructure cost projections
- Technical team and hiring gap assessment
- Risk identification and mitigation plan
Best Fit
Engagement Models
Architecture Sprint
You need the right architecture defined before your team starts building. Deliverable: architecture doc, stack decisions, cost model.
Build Partnership
You need an experienced technical lead who can make decisions and build. Best for early-stage AI-native products.
Ongoing Advisory
Your team is executing but you want experienced oversight on architecture, hiring decisions, and technical strategy.
Let's talk architecture.
30-minute call. Bring your hardest technical decision and we'll work through it. If there's a fit, we'll define the engagement from there.
Book a Discovery Call