Services
Six practices that compose: agentic AI and MCP on top, data science and evals in the middle, twenty years of cloud and web engineering at the bottom — delivered by the person you actually talk to.
What I do
Capabilities offered as services. Where a number appears, it comes from a real, named engagement — not a projection.
Agent fleets with shared memory, MCP servers that expose your internal systems as safe, scoped tools, human-approval gates on every side-effect, and session orchestration. I run my own multi-project operation this way — twenty-plus agent sessions, one shared memory — so the patterns arrive already broken in.
RAG over private knowledge bases, local-first deployment with Ollama and llama.cpp, model selection driven by evals, and context engineering that keeps answers grounded in your data — not the model’s imagination. Your data never has to leave your hardware.
Calibrated forecasting, customer-behavior analytics, BI dashboards in Looker, Tableau, and Grafana, automated alerting, and evals & metrics design for AI systems — on a certified data stack.
n8n pipelines with LLM steps, self-hosted marketing automation (Mautic plus AI), and process monitoring — the ops glue that keeps automations from quietly rotting between runs.
AWS multi-region delivered at a documented 99.99% uptime across US-East and US-West, HIPAA- and SOC II-shaped designs with audit trails, blue-green deploys with DNS failover, and model-serving infrastructure — with twenty years of web platforms underneath, including page loads taken from 8+ seconds to under 2.
Kalshi API pipelines and MCP servers, arbitrage detection, and calibrated-probability tooling — where models meet money and get graded in public. Built and run as ongoing personal research, available as an engagement.
Who I help
From regulated pharma platforms to streaming media to quant research — the common thread is an AI system that has to hold up in production, not a demo.
Teams that want agents, not chatbots
You’ve done the copilot pilot. Now you want an agent that actually files the ticket, updates the CRM, and reports back — behind approval gates your compliance team can sign off on.
Founders & small teams
You have an idea, a pile of APIs, and no appetite for a department. One senior person designs the agent, builds it, runs it — and hands you the keys, prompts and evals included.
Regulated industries
Healthcare, pharma, finance. I built a pharmaceutical B2B platform with full audit trails for controlled-substance orders — the same discipline now decides what an agent may touch.
Publishers & media
From a financial publisher’s digital transformation to NBA streaming on Roku — now with content pipelines that carry LLM steps and dashboards on what readers actually do.
Marketing & growth teams
Self-hosted Mautic with automated IP warming took a crisis list from 12% back to 31% opens and 4.2% CTR. Add LLM steps for segmentation and drafting — on sending reputation you own.
Subscription businesses
Customer-behavior analytics for retention: calibrated churn signals, dashboards your team actually reads, and alerts that fire before the quarterly numbers notice.
Quant & research desks
Prediction-market pipelines, MCP servers, and calibrated-probability tooling — for people who want models and markets talking, with the calibration curve to prove it.
Measured outcomes
Two examples of before-and-after, measured on live systems.
Digital transformation with a CloudFront CDN and automated testing, code review, and deploy pipelines.
A deliverability crisis fixed with self-hosted Mautic and automated IP warming.
Full context for each figure on the case studies page.
How I build
Current focus: agent fleets, MCP servers, eval pipelines, local-first LLMs, n8n automation with LLM steps, and prediction-market tooling.
Model choices, prompts, and agent behavior get graded by eval suites, not adjectives. If quality isn’t measured, it’s a mood.
Agents propose; approved actions execute; refusals get logged. Nothing writes to production without a witness.
Ollama and llama.cpp before API bills. Small-but-good models on hardware you control — cheaper, private, yours to keep.
Dashboards and alerts with the first deploy, audit trails by design. If a regulator asks how it works, there is a document.
Start here
Tell me what you’re trying to do. First conversation is free.