§ Data Science Engineering·United States

Data science engineering services.

Applied data science and ML engineering for US teams that need a model in production, not a notebook on a laptop. We build the evaluation, the pipelines, and the data foundations that move data science from experiment to system — fixed-fee, fixed-scope, senior-only, with full ownership handed back to you. Where most firms stop at the model, we engineer the part that actually ships.

Start with an AI Readiness Sprint → See the engineering practice → serving clients across the United States

A model is not a system. Engineering is the gap.

The hard part of data science was never the algorithm — it's everything around it: trustworthy data, a real evaluation harness, a deployment path, and the observability to know when a model drifts. That's the difference between data science and data science engineering, and it's where most initiatives quietly stall.

85%
of AI and data-science projects fail on poor data quality and governance (Gartner). The science is rarely the bottleneck — the engineering and the data foundation are.

So we start by reading the foundation. The AI Readiness Sprint tells you whether your data, infrastructure, and use case can actually support a production model — and what to build first. Then we engineer it: evaluation, pipelines, and a system your team owns.

§ What we build·data science engineering
Applied data science
Forecasting, classification, ranking, anomaly detection — scoped to a decision your business actually makes, with the metric that proves it works.
ML engineering
Feature pipelines, training and retraining, serving, and the CI gates that block a bad model before it reaches users. The plumbing that turns a notebook into a service.
Evaluation & eval harnesses
Golden-case suites, quality metrics, and prompt/model regression in CI — so you ship on evidence, not gut feel. The same rigor we apply to our own RAG evaluation harness.
Data foundations for modeling
The pipelines, quality checks, and governance a model depends on. Because 85% of projects fail on the data, not the science.
Production AI & LLM systems
RAG, agents, and multi-tenant AI services — applied data science taken all the way to a deployed, observable, owned system.
Observability & drift
Per-model traces, cost, and drift tracking, with alerting from day one — so a degrading model is a signal, not a surprise.
Start here
AI Readiness Sprint
A fixed-scope read on whether your data and infrastructure can support a production model — maturity scorecard, shadow-AI audit, and a prioritized 90-day roadmap, from $12k.
Scope a Readiness Sprint →
Build it
AI Engineering
The production system on top of the science — RAG, agents, pipelines, eval harness, observability, and a runbook that outlives the engagement.
See the engineering practice →
Read first
Why AI adoption is failing
Why most data-science and AI initiatives stall before production — and what readiness looks like before you commit a build budget.
Read the framework →

The people who model it are the people who ship it.

No hand-off from a data-science team to an engineering team that never talked. One senior bench owns the model, the pipeline, and the deployment — fixed-fee, with a runbook on exit. If you need data science that actually reaches production, that's the whole point of how we work.

Scope a call → See all services →