Machine learning that tells you something useful — which customers are likely to convert, where your funnel is losing people, what your data predicts about behavior you care about. Trained on your actual data, integrated into your actual systems.
A lot of companies will sell you a machine learning engagement. What you get is a model that performs well on a test dataset, a report full of metrics you don't fully understand, and a system your team can't maintain without the vendor in the room.
We've built production ML systems that live inside real business workflows — trained on messy, real-world enterprise data, producing outputs that go directly into decision-making. That means understanding the business question first, then working backward to the model.
We're not a research lab. We're not going to propose a novel architecture for a problem that XGBoost solves in an afternoon. We use the right tool for the problem, we validate it honestly, and we build it so you understand what it's doing and why.
If your problem isn't a good fit for ML, we'll tell you that too.
These are the problem types we've worked on with real data at production scale — not capabilities copied from a vendor brochure.
Classification models that score customers or leads by likelihood to convert — trained on your historical behavior data. The result is a ranked list your sales or marketing team can actually act on, not a probability score that lives in a notebook.
Models that classify user or customer behavior — bounce vs. engage, churn risk, high-value vs. low-value segments — using your actual event and transaction data as features. Built to run at scale across your full dataset, not just a sample.
Markov chain models and sequence analysis that map how customers or users move through your funnel — identifying high-dropout paths, predicting next likely actions, and surfacing where behavioral patterns break down.
The data engineering layer that makes a model actually useful in production — automated feature extraction from your source systems, scheduled retraining, and prediction output delivered back into whatever system needs it (database, API, dashboard).
Prediction scores written back to your data warehouse, surfaced in your BI tools, or exposed via API — so the model doesn't live in isolation. The whole point is that someone or something uses the output to make a better decision.
Honest performance reporting — precision, recall, lift curves, and business-level validation that tests whether the model actually improves outcomes, not just whether it scores well on held-out data. We don't cherry-pick metrics.
Every step is designed around one question: will this actually be useful when it's deployed?
We start with the business question, not the model. What decision are you trying to make better? What does a good outcome look like? This conversation often changes the scope of the project — sometimes it reveals ML isn't the right tool, and we'll say so.
We audit your available data, assess label quality, identify what signals are actually predictive, and build the feature set. This is where most ML projects succeed or fail — a well-engineered feature set outperforms a fancy model on poor features every time.
We train the model, tune it, and evaluate it against metrics that matter for your use case — not just accuracy. For imbalanced classification problems (which most real business problems are), we're explicit about precision vs. recall tradeoffs and what they mean for how you use the output.
We deploy the model into your environment, build the pipeline that keeps it fed with fresh data, and connect the output to wherever it needs to go. Tracked with MLflow so model versions are reproducible and rollback is possible if behavior drifts.
We keep the stack practical. These are tools we've used in production, not a list assembled to look impressive.
That's the conversation. 15 minutes to tell us what you're working with — we'll tell you honestly whether a model makes sense and what it would take to build one that's actually useful.
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