I design and ship AI-assisted workflows, opportunity audits, and custom copilots that run unattended and hold up under load. Backed by 25 years of engineering depth.
Cut a client's cloud bill from $50K → $7K/mo by re-architecting the pipeline around agents.
Email throughput unlocked. 50M sends a month on a system that used to choke.
Ad impressions served per day at scale. Driving a +40% revenue lift.
Design and ship AI-assisted workflows that reduce manual work, route decisions, and keep humans in the loop where judgment still matters. Tool-calling, orchestration, guardrails, and evaluation, not notebook demos.
Review your current process, identify where agent automation is actually worth pursuing, and map the technical and operational shape of a solution before you overbuild.
Build bespoke agents, copilots, and AI-enabled product features. Then improve quality, routing, retrieval, latency, and cost so the system performs reliably in production.
Architected a conversational AI sales experience guiding users through personalized health assessments that generate tailored nutrition and workout plans.
Developed an AI transcription platform combining Go-based Whisper CLI for local audio/video transcription with a FastAPI backend orchestrating asynchronous processing via RabbitMQ worker pipelines.
Built an AI-driven specification generation tool that converts loosely defined product ideas into structured engineering specifications through iterative clarification workflows.
Map the workflow you're trying to automate or augment, and where an agent actually helps versus adds risk.
Design the agent and tool boundaries, state management, and guardrails before writing code.
Ship in small, testable slices, instrumented for evaluation from day one.
Leave your team owning it; docs, runbooks, and clear escalation paths.
I start with the business problem, not the technology. AI is a tool, not a destination.
Ship things that run unattended, not proofs of concept that need a handler.
Understand the workflow before writing code, most AI projects fail here, not at the model.
Agents augment judgment where it's expensive; they don't replace it where it matters.
Evaluation, guardrails, and cost controls are part of the build, not an afterthought.