Services

Four engagement types. Each is scoped on the call, priced after the scope, and shipped against a clear definition of done. No retainers we can't justify, no pricing page that anchors before we've talked.

• AI and ML systems engineering

  Production ML pipelines, RAG, model serving, eval coverage, and the cost discipline most "AI consultants" skip.

  The interesting work is rarely the model. It's the ingestion path, the eval set you trust, the guardrails, the cost ceiling, the fallback when the API has a bad day. We build the system around the model so the model can be wrong without taking the product down. Python, PyTorch, Hugging Face, FastAPI, plain SQL where it earns its keep.

  What done looks like: A model in production with eval coverage you trust, a documented cost profile, and an honest answer to "what happens when it's wrong."

• Agent architecture and velocity engineering

  Contract-first agent fleets, autonomous coding workflows, and the human-in-the-loop gates that keep them merging cleanly.

  The pattern behind our own agent-driven delivery system: small, contract-defined units of work that agents pick up in parallel, with humans on the merge button instead of the typing keyboard. We install the workflow, train it on your codebase, and hand back the runbook. The interesting work is in the contracts, the gates, and the failure modes. We've spent real cycles on all three.

  What done looks like: A running agent loop on a real branch of your repo, merging real work through real review gates, plus the documentation your team needs to keep it healthy after we leave.

• Data science strategy and decision systems

  Turn the data you have into the decisions you need. Analytics, decision-support surfaces, and the discipline of asking the right question first.

  The hard part isn't the dashboard. It's deciding which number a leader actually has to look at, and trusting that the number got there honestly. We default to plain SQL and small models, and we know when the problem actually calls for a larger one. The through-line from decision-support work delivered during prior employment, brought forward with an AI/ML stack underneath.

  What done looks like: A decision surface a real leader uses without translation, a documented data lineage, and a measurable change in how fast a call lands.

• AI safety, alignment, and ethics consulting

  Threat modeling for autonomous systems, the human-in-the-loop line, and what to never automate. Drew's research thread brought into the engagement.

  The questions about how autonomous systems should and shouldn't be built belong upstream of the code, not in a press release after the incident. We help draw the line on which decisions are eligible for automation at all, audit-trail the ones that are, and design the kill switches before they're needed. This isn't a manifesto and it isn't a posture; it's engineering that takes the failure modes seriously.

  What done looks like: A documented safety posture for an AI feature your team is shipping, an audit trail your compliance team can read, and a defensible answer to "who's responsible when this thing is wrong."

Not sure which of these you're asking for? That's fine, most clients aren't. Tell us what you're trying to do and we'll name the engagement type for you.