Data & AI Consulting
I build data platforms, analytics, and LLM features that hold up after launch: scoped to the real problem, built to be maintained, and handed to a team that can run them without me.
What I Do
I work with leadership and engineering teams to scope, build, and ship data and AI systems, and to make sure they're still earning their keep six months after launch.
01 ——
The foundation everything else rests on. I design the data platform your analytics and AI will depend on, whether you're starting from scattered spreadsheets or untangling a decade of one-off systems.
02 ——
Practical ML, sized to the problem in front of you: document understanding, agentic validation workflows, and custom PyTorch and TensorFlow models when off-the-shelf won't cut it.
03 ——
Reporting that answers the questions your team is actually asking, built on operational data you'd be comfortable defending in a board meeting.
04 ——
LLM features with a payoff you can measure. Scoped tightly, evaluated against real user behavior before launch, and shipped with the guardrails to keep them reliable after it.
05 ——
Pipelines, transformations, and warehouse design that deliver correct data on schedule, so your analysts and models stop waiting on yesterday's numbers.
06 ——
Fractional data leadership, architecture reviews, or a senior engineer embedded in your sprints. As much or as little as your team needs, for exactly as long as it's useful.
Selected Work
A few engagements, with the outcome that mattered to the client. Details are anonymized.
Test results from 12 machines lived in spreadsheets and were stitched together by hand; a single report took days. I built a system that catalogs every test as it runs. Recording a result now takes seconds, any past run can be pulled on demand, and the days of Excel work behind each report are gone.
Verifying how much carbon a process had captured took a contracted review team 4–6 weeks. I built an agent system that produces the full calculation autonomously in 45 minutes, with a human reviewer signing off on the result. In side-by-side tests it landed within 10% of the reviewer's independent figure. Reviewers now start from a finished report instead of raw data, cutting each review contract by 2–4 weeks and the cost that comes with it.
A weekly batch job meant every report was built on stale data nobody fully trusted. I moved their lake to a streaming medallion architecture with clear lineage. Data is now current, and reports that took weeks to assemble take minutes.
About
I'm Chris Maness, a principal-level engineer specializing in data and AI. I take on a small number of consulting engagements at a time, so each one gets my full attention. Over the past decade I've built data platforms and the products that run on them at every scale: a startup's first data stack, enterprise streaming data lakes, medallion architectures, and LLM and agentic systems carrying real production load.
Greenfield pipeline or years of accumulated technical debt, the approach is the same: see what's really there, be honest about what it needs, and get to work.
The goal is to leave behind systems your team can own: documented, maintainable, and understood by the people who'll be running them long after I've moved on.
How I Work
I've built inside SOC 2 and ISO 27001 environments, on U.S. Department of Energy projects, and with HIPAA-protected data. Vendor onboarding, security questionnaires, NDAs, and your existing change-management process are part of the job, not obstacles to it.
Let's Talk
Send a short description of the problem. You'll hear back from me directly, within one business day, with a straight answer on whether I can help.