AI Systems
Ship AI features that stay reliable in production
I design and implement AI systems that sit inside production products: ticket triage, recruitment matching, conversational assistants, and recovery workflows. The focus is reliable orchestration, clear service boundaries, and observability—so AI work is maintainable after launch.
Who this is for
- ›Product teams adding LLM workflows to an existing SaaS product
- ›Founders who need an end-to-end AI feature without a full in-house AI team
- ›Engineering leads who want production guardrails, not prototype notebooks
Capabilities
How engagements run
01
Scope the decision path
Clarify what the model should decide, what humans must review, and what failure looks like in the product.
02
Design the service boundary
Separate AI workloads from core business logic so each can scale, deploy, and fail independently.
03
Build and instrument
Implement the workflow, wire it into the product UI/API, and add the observability needed to debug real traffic.
04
Harden and hand off
Tune prompts and recovery paths, document ownership, and leave a system the team can operate.
Good fit
- ›You already have a product surface that needs AI assistance
- ›You care about latency, reliability, and reviewability
- ›You want architecture that can grow beyond a single model call
Not a fit
- ›One-off chatbot demos with no product integration
- ›Research-only model training without an application context
Related work
Ready to talk through a problem?
Share the product context, constraints, and what success looks like. I reply to selected engagements that are a clear fit.
Discuss an AI product engagement