AI-first MVPs. Shipped fast. Built like production.
I build agentic workflows, post-training where it pays, prompt/eval systems, and LLM cost controls—without sacrificing TDD, CI/CD, or iterative delivery discipline.
What I Actually Build
Agentic workflows (not demos)
Autonomous loops that handle complex business logic without hallucinating.
Post-training when it pays
Fine-tuning and prompt optimization backed by evals that prove ROI.
LLM cost + reliability controls
Routing, caching, and budget enforcement that cuts spend without breaking quality.
Offers
MVP Sprint
A thin slice to production: auth, core workflow, telemetry, and deployment—nothing more, nothing less.
- Working authentication flow
- Core workflow end-to-end
- Observability + tracing
- Production deployment
Agent Build
One agent workflow, shipped end-to-end with tools, safety rails, and an eval suite.
- Typed tool integrations
- Safety constraints + guardrails
- Eval harness (30–100 scenarios)
- Deployment + runbook
Cost & Reliability Tune-Up
Cut your LLM spend without breaking quality. Routing, caching, prompt tests, and budget enforcement.
- Model routing logic
- Response caching layer
- Prompt regression tests
- Cost monitoring + alerts
How It Works
Scope + Success Metrics
We define the workflow, success metrics, and kill criteria together.
Daily Demo Loop
Short iteration cycles with daily demos. You see progress, I get feedback.
Tests, Evals, Tracing
Every change is tested. Evaluations run in CI. Cost budgets are enforced.
Deploy + Handover
Production deployment, documentation, and a runbook you can actually use.
Proof
Results from real engagements (anonymized).
Agent Workflow for Legal Tech
MVP for AI Writing Tool
Cost Optimization for SaaS
Insights
Thoughts on building AI systems that actually work.