Tell me what you’re trying to build.
Bring a specific problem, or just describe your product. I’ll show you what to build, what to automate, and how I’d ship it — agents, RAG, automations, full products.
Operating model
Systems-first AI engineering, from brief to production.
The portfolio is built around the work: agent orchestration, RAG pipelines, automations and product interfaces that can be inspected directly. The leverage comes from a swarm of agents that researches, codes, reviews and ships in parallel, so a single operator can move with the cadence of a compact studio.
See the methodWhat I do
Five ways I turn AI into shipped products
From a single autonomous agent to a full multi-cloud product — design, engineering and ops under one roof.
AI Agents & Swarms
Autonomous systems that plan, act and coordinate.
I design multi-agent systems with LangGraph: an orchestrator that delegates to specialized sub-agents, with tools, memory and auditable decision logic. Not a chatbot — a system that does the work.
- Orchestrator + specialized sub-agents (LangGraph)
- Tool calling, memory and state management
- Human-in-the-loop checkpoints and audit logs
- Parallel execution tuned for cost and latency
RAG & Knowledge Systems
Answers grounded in your own data.
Retrieval-augmented pipelines that turn messy documents into trustworthy answers: ingestion, chunking, embeddings, hybrid search, re-ranking and citations — so the model stops guessing.
- Ingestion + chunking + embedding pipeline
- Hybrid (vector + keyword) retrieval & re-ranking
- Cited, grounded answers with eval harness
- Vector DB setup (pgvector, Pinecone, Qdrant)
Multi-Cloud & DevOps
Shipped, scaled and observable.
I deploy and operate AI workloads across GCP, AWS and Azure: containers, serverless, CI/CD, infra-as-code and observability. The system runs reliably and you can see exactly what it’s doing.
- Containerized deploys (Docker, Cloud Run, ECS, AKS)
- CI/CD pipelines and infra-as-code
- Secrets, cost guards and rate limiting
- Logging, tracing and alerting for LLM apps
Automations & Skills
Stop doing by hand what a system can do.
I find the repetitive work in your operation and wrap it in reproducible skills and scheduled automations — same input, same output, every time, plugged straight into the tools you already use.
- Reproducible, versioned skills
- Scheduled & event-driven automations
- Integrations with your existing tools & APIs
- Docs and handoff so it lives in your stack
Full AI Product Design & Build
The whole thing — UX, code and the AI inside.
When you need the complete product: I design the interface, build the full-stack app and embed the AI so it feels like one coherent thing. Design taste and engineering depth from the same person.
- Product & UX design (Figma → shipped)
- Full-stack build (Next.js, TypeScript, APIs)
- AI features embedded natively in the UX
- From prototype to deployed, polished product
Selected work
Systems, agents and products I’ve built
A selection of AI systems I designed and engineered end-to-end — from RAG knowledge engines to autonomous agent swarms.
Personal Agent Swarm
My own fleet of agents: an orchestrator that delegates research, coding, review and publishing to specialized sub-agents running in parallel. It’s how I ship like a studio of one.
RAG Knowledge Engine
A retrieval engine over large, messy document sets. Hybrid search and re-ranking return grounded, cited answers — with an eval harness that catches regressions before they ship.
Inbox Spend Tracker
An agent that scans an inbox daily, detects every purchase and recurring charge, normalizes currency and pushes spending into a personal dashboard. Read-only, zero spreadsheets.
By the numbers
A track record, not just a title
Let’s build
Have an idea that needs AI to actually work?
Tell me what you’re trying to build. I’ll bring the design, the engineering and a swarm of agents to ship it.