Applied data science services for US mid-market 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 machine learning 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. Machine learning consulting from the people who write the architecture, train the model, and deploy it.
The hard part of machine learning 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 a data-science experiment and applied data science delivered as a production system, and it's where most initiatives quietly stall. A model that scores well in a notebook and never reaches a user is a sunk cost, not a result.
Mid-market teams feel this most acutely. You have the data and a use case worth funding, but not a standing ML platform team to carry a model from prototype to production and keep it healthy afterward. The work falls between your analysts, who built the model, and your engineers, who never owned it — and the initiative dies in the handoff. Machine learning consulting, done right, closes that gap: one senior bench owns the model, the pipeline, and the deployment end to end.
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. No re-platforming for its own sake, no model that can't be retrained, no dependency on the consultant who built it.
Every applied data science engagement is scoped to a named outcome and a fixed fee. The baseline build covers the work that actually moves a model into production and keeps it there.
We are not a staffing shop and we do not bill bodies by the hour. Machine learning consulting runs as fixed-fee, fixed-scope sprints, typically four to twelve weeks, with a runbook on exit. Most engagements start with a readiness read, then move into the build.
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.
The model, the pipeline, the eval harness, and the deployment — scoped to a fixed fee off the readiness read so you approve a number, not an open-ended retainer.
Golden-case regression, drift alerting, and retraining cadence so the model stays as good as the day you shipped it — handed to your team to run.
How we stop the million-dollar chunking mistake — the evaluation rigor that separates a model that demos from a model that ships.
Read the framework →How enterprise AI deployment actually gets architected from scratch — the path from a model to a system your organization can run.
Read the framework →An edge-to-cloud predictive-maintenance framework for manufacturing — applied data science taken to a deployed, monitored system.
Read the framework →AWS-native by default — SageMaker, Bedrock, Lambda, Step Functions — with bring-your-cloud on request. Where a team has standardized on the Lakehouse, we run the modeling and MLOps on Databricks: Unity Catalog for governance, MLflow for tracking and registry, and Mosaic AI for the production model surface. The tooling serves the outcome; we don't re-platform a team that's already productive.
Engagements are fixed-fee and scoped up front. Most start with an AI Readiness Sprint from $12k (four to six weeks), which sizes the build before you commit to it; the ML build that follows is scoped to a fixed fee off that read, typically a four-to-twelve-week sprint. You approve a number, not an open-ended retainer.
A hire builds a model; applied data science delivers a system. We bring the evaluation harness, the deployment path, and the observability a single analyst rarely has time to build — and hand it back with a runbook so you are not dependent on us afterward.
Both. Forecasting, classification, ranking, and anomaly detection are most of the work that actually moves a business metric. We reach for an LLM when the problem calls for one, not by default.
That is the most common finding, and the Readiness Sprint exists to catch it. If the foundation needs work first, we sequence the data engineering ahead of the modeling so the first build is the one most likely to pay off.
You do — full IP transfer, training pipelines, eval suite, and a runbook. Senior-only delivery means the person who scopes the work is the person who builds it, and they hand you the keys on exit.
Yes. We pair with your team through the build so the knowledge transfers, then step out. The goal is a system your engineers can retrain and operate, not a dependency on our bench.
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 machine learning that actually reaches production, that's the whole point of how we work.
It matters who does the work. The reason most machine learning consulting disappoints is that the model and the system are owned by different people who never share context — the analyst optimizes a metric the deployment can't serve, and the engineer ships something the analyst never validated. A senior-only team that carries the work end to end closes that gap by construction: the same judgment that picks the right baseline also designs the eval harness, the serving path, and the drift alerting. You get applied data science that holds up under real traffic, and a model your own engineers can retrain long after we're gone.