We build and ship machine-learning models trained on your own data - forecasting, scoring, recommendation, classification and computer vision - to power a feature in your product or a decision in your business. Where our agents and copilots reason and act, these models predict and decide. Built to run in production, monitored and governed, not left in a notebook.
A model that scores well on a data scientist's laptop isn't a model that's making decisions in your product. Getting ML into production - trained on your real data, integrated into your systems, watched for drift, and trusted enough to act on - is where most efforts stall. And a generic, off-the-shelf model rarely fits your data or your problem.
A model that scores well in an experiment but never gets integrated, deployed or actually used.
Off-the-shelf or generic models that miss the patterns specific to your business and your data.
Accuracy quietly degrades as the world changes - and no one is watching for it.
No monitoring, no explainability, and nothing to justify a prediction when it actually matters.
Four families of model, one discipline - trained on your own data, validated beyond a leaderboard score, and built to run in production.
Models that forecast demand, revenue, churn, risk or failure from your historical data - so you act ahead of what's coming.
Models that score and classify - risk, fraud, lead quality, defect, case triage - turning raw data into a decision.
Recommendation and ranking models that personalize what each user sees, buys or does next.
Vision models that read images and video - classification, detection, OCR, and quality or defect inspection.
Every model follows the same disciplined path - frame it, ground it in real data, train and validate it honestly, ship it, then keep it accurate. Tap through each stage, or let it play.
We define the decision the model serves and the metric that matters - and call build-vs-buy honestly before anyone trains anything.
A generic view of how we take a model from your data to a decision in production - the shape holds whatever the problem. Data flows in two ways: a batch from your warehouse, or live signals from your systems. A feature pipeline engineers the signal, a validated model is trained and registered, and predictions go out two ways - streamed live, or scored in batch. Governance, explainability and drift monitoring wrap the whole thing.
Validated for accuracy, robustness and bias before it ever serves a real decision.
Two ways out, one system - a prediction served live, or a batch scored on a schedule, with a retraining loop underneath.
The loop is the point. The same model serves a prediction in real time and scores in batch - while a monitoring loop watches for drift and retrains it as the world changes. That's what separates a model that ships from one that dies in a notebook.
The hardest part of ML usually isn't the model - it's the data: clean, joined, engineered and flowing reliably. We build models on a real data foundation, not a one-off extract from a spreadsheet. Where the foundation needs work first, our Data Engineering practice and our Databricks partnership get it ready, so the model has something solid to learn from.
The foundation: Data Engineering→The tools we use across the lifecycle - from data and features through training and vision to serving and monitoring - with the strong alternatives at each layer, shown together. We pick per problem rather than forcing one house style.
The standard ML/MLOps toolchain, anchored by our Databricks and Snowflake partnerships - a representative set. We pick per problem rather than forcing one house style.
The gap most ML dies in is the one between a working model and a deployed one - and that gap is engineering, which is what we do.
A Databricks partnership and proper data engineering, so models run on engineered data - not a one-off extract.
Validation beyond a leaderboard score - robustness, bias and explainability - plus monitoring for drift once it's live.
Framing the problem, the data, the model, the deployment and the monitoring - from one team, not handed off piece by piece.
ISO 27001 processes and a partner stack to match - Claude Partner Network, Snowflake and Databricks - for the data-and-AI foundation underneath.
Usually a CTO, VP of Engineering, Head of Data, Data Science or AI, a product leader, or an operations leader.
When a model scores, flags or shapes a decision, you have to be able to explain it, show it isn't biased, and prove it's controlled - for US frameworks like the NIST AI RMF, the EU AI Act, and your customers' reviews, wherever you operate. We validate for robustness and bias, build in explainability, and monitor for drift - so the model is one you can actually stand behind.
More on this: AI Governance→A model rarely stands alone. It needs a data foundation underneath, monitoring and retraining to stay accurate, and it often sits inside a product feature or an agent that acts on what it predicts. Most ML work starts with one high-value decision and grows from there.
Agents and copilots reason and act using LLMs. ML Development builds models trained on your data to predict, score, classify or see - the model behind a specific decision. They often work together: a model scores, an agent acts.
Models run on data. If yours isn't ready, our Data Engineering practice and Databricks partnership get it ready as part of the work - the model needs something solid to learn from.
Often. We'll tell you when an off-the-shelf model or an API is the right call, and when your problem genuinely needs a custom one trained on your data.
Monitoring for drift and retraining as the world changes. That ongoing run is covered under ML/LLM Ops.
Part of how we validate and govern every decisioning model - see AI Governance.
An NDA up front, ISO 27001 controls throughout, and your data and any models built on it stay yours.
Forecasting, scoring and classification, recommendation and ranking, and computer vision - trained on your own data.
Book a 30-minute discovery call. We'll find the highest-value decision a model could make better - and build one that ships.