Outcomes over output
Every engagement starts with the decision being made and the metric being moved. If we cannot name those, we do not start.
Independent data, ML, and agentic AI practice
Real AI systems. Not demos.
Ballou Analytics is an independent data, ML, and agentic AI practice. The AI market is full of hype. I build calibrated, measured, production-grade systems that move metrics - and tell you when AI is not the right answer.
No fluff. No hype.
We do not sell magic. We ship systems that work.
AI is noisy right now. Value comes from the unglamorous parts: pipelines, evals, guardrails, docs, and handoff that turn a model into something a business can actually depend on.
Calibrated, not confident
Models that know what they do not know. Intervals, refusal policies, and honest error analysis - not demo-day theater.
Production-grade or bust
Anything I ship has tests, observability, runbooks, and a rollback path. If your team cannot keep it running, I have not done my job.
Plain English
Tradeoffs get translated for the people writing the check. No jargon stack, no architecture astronautics, no AGI handwaving.
Agentic AI
Agentic AI, without the magical thinking.
Everyone is selling agents right now. Few are running them in production. I build measured, observable systems that do useful work - and tell you when an agent is not the right answer.
Tool-using agents
Constrained, observable agents that call your APIs, databases, and internal tools with rollback, audit trails, and a clear blast radius.
Retrieval pipelines
RAG systems over your own corpus, with citation enforcement, freshness controls, and the eval harness that proves recall is what you think it is.
Structured extraction
Document intelligence that returns typed, validated objects - not vibes. Field-level confidence and human review where it earns its keep.
Orchestration and evals
Multi-step workflows with cost ceilings, latency budgets, replayable traces, and golden sets that catch regressions before users do.
About
Operator, not agency.
Nathan Ballou - Founder & Principal
I am a generalist across operations research, data engineering, and data science: the kind of operator who can shape the decision model, ship the pipeline that feeds it, and explain the tradeoffs clearly.
Most of my work sits in the gap between a clever notebook and a production system the business trusts. That gap is where consulting tends to fall apart and where I tend to be useful.
Contact
Tell me what is in your way.
Send a short note about the decision, system, or workflow you are trying to improve. I will reply with whether and how I can help.