I take AI from prototype to production
I love helping teams turn technology into real leverage: AI wired efficiently into their workflows, gated by evals, and handed off as a system they can trust and run. Voice agents, LLM pipelines, agentic automation.
Most teams have AI tools. Few have AI leverage.
The gap is not access to models, it is getting them into production: wired into real workflows, measured, and trusted. That is the work I do.
- Prototype with no path forward
- No evals, no trust
- Not wired into real data
- Works in demo, fails in prod
- No observability
- Brittle integrations
- Unmeasured outputs
- Never handed off to the team
I close the gap: wire the model into real workflows, add eval gates, and hand it off as a system the team can trust.
WHAT I BUILD
From prototype to production.
Voice Agents
Answer, qualify, and route every call in real time. Multi-tenant, with consent and call-outcome tracking built in.
LLM Pipelines
Turn your data into answers and actions: RAG, tool use, and multi-step agents wired into the systems you already run.
Eval Gates
Know what ships actually works. Model-judged gates with versioned rubrics where code decides pass or fail, not vibes.
Agentic Automation
Hand whole workflows to agents that ingest, decide, and act on a schedule, with operator approval on anything irreversible.
Multi-tenant SaaS
Ship a platform that runs itself: row-level security, atomic billing, and webhook integrity by default.
Data Pipelines
Get clean, enriched data on a budget: scraping, storage, and analysis with per-run cost caps designed in.
Forward-Deployed Delivery
I embed with your team and turn messy operations into shipped AI systems you own.
MCP and Integrations
Plug agents into the tools and systems your business already runs on.
Prompt Engineering
Reliable outputs in production: systematic prompt design, model selection, and structured-output tuning.
Observability
See what your AI is doing and what it costs: structured logging, cost tracking, and latency dashboards.
I scope to one system at a time: ship it, measure it, then expand.
The Operating Loop
Messy workflow → trusted production system.
Not a one-time build — a continuous engine. Deployment is step 4, not the finish line.
Fix one workflow and the next bottleneck shows itself. The gains compound across the whole operation — and the loop never stops.
Where I've Shipped
Across industries, one pattern: get AI into production.
Different domains, same discipline: find the highest-leverage workflow, build the agent, ship it.
Healthcare
Logistics & Fulfillment
Legal
Retail & E-Commerce
Sales & GTM
Professional Services
Trading & Fintech
Startups & AI-Native Teams
From regulated healthcare to fast-moving startups. The work translates.
My Stack
Four layers I build across.
Voice, browser, MCP, and coding. Each layer targets a distinct class of problem. Together they cover most of what a modern AI build requires.
Voice
Real-time conversational agents.
Consent, routing, call-outcome classification.
Browser
Agents that operate the tools
a business already uses.
MCP
Model Context Protocol integrations
connecting agents to live systems.
Coding
Agentic coding workflows across
the full build cycle.
- ✓Each layer can be engaged independently or together.
- ✓Most projects start with one layer and expand from there.
- ✓All four share the same underlying agent architecture.
BUILT WITH
The modern AI engineering stack.
The tools I reach for to ship agentic systems: typed, fast, and production ready.