You already have the data. You just have not asked it the right question yet.
We build classification, regression, and forecasting models on your structured data and turn numbers in a database into decisions you can act on.
Live scoring
Data flows in. The model scores it. Results surface on the right.
The problem
Not every prediction problem needs a neural network.
Most business prediction problems are not image recognition or language generation. They are questions like: which customers are about to leave, which transactions are fraudulent, what will demand look like next quarter. These questions have structured answers in structured data: rows and columns in a database or a spreadsheet that you already have.
The default instinct is to reach for the most complex tool available. A transformer, a deep network, something that sounds advanced. For tabular data, gradient-boosted trees almost always win on accuracy, train in minutes rather than hours, and run inference on a laptop. The complexity you skip is not a shortcut, it is the right choice.
The actual hard part is not the algorithm. It is the features. The columns you include, the ones you engineer from the ones you have, the ones you drop because they leak information the model would not have at prediction time. That is where the accuracy lives. Building a model that performs well on holdout data and then degrades on production data is almost always a feature engineering problem, not a model problem.
Here is the version you might recognize. Someone on your team already built a churn model in a notebook. The numbers looked great: 96% accuracy on the test set, charts that impressed the room. You shipped it. Three weeks in, it is flagging customers who already canceled and missing the ones who quietly drift away. Nobody touched the code. The model did not break. It was scoring well in the notebook on information it will never have when the prediction actually matters. That gap between the demo and the deploy is the whole story, and it is almost never the algorithm's fault.
The silent killer
The feature that secretly knows the answer.
Data leakage is when a feature carries information the model would never have at the moment it predicts. The classic teaching case: a pneumonia model fed a column for whether the patient took antibiotics. Every patient who took them had pneumonia, so the model "learned" a rule that is really just the answer wearing a disguise. You get 99% in testing and noise in production, because at prediction time you do not yet know who took the antibiotic. The model was not smart. It was peeking.
It hides in ordinary columns. A "number of support tickets" field that quietly includes the cancellation ticket. A timestamp updated the day the fraud was confirmed. A patient ID that happens to encode which hospital, and therefore which disease. None of these look wrong in a spreadsheet. They look like signal. That is exactly why leakage is dangerous: the better your score, the more you should suspect it. A churn model that scores near perfect is not a triumph. It is a clue that a feature is cheating.
The second silent killer is train/serve skew. The feature is honest, but it gets computed one way in the training notebook and a different way in the live pipeline. A "7-day average" built offline over the full history, then truncated online to whatever is in cache. Nulls forward-filled in the Python that trained the model, treated as zero in the service that scores it. The model is fine. The plumbing lies to it. Google reported a version of this with a diabetic-retinopathy model that scored beautifully in the lab and stumbled on the messier images real clinics actually produce. We close that gap by computing each feature with the same logic at training time and prediction time, then auditing every column with one question: would you know this value the instant before the outcome happens, or only after?
How we build it
Features first, model second.
We spend more time on your data than on your model. These are the patterns we work with.
A dumb baseline you have to beat
Before any model, we write the rule a sensible person would use without machine learning. Predict next month's demand equals last month's. Flag every transaction over a threshold. Call every customer who has not logged in for 30 days a churn risk. That baseline is the bar. If a trained model cannot clear it, the model is not earning its complexity and we say so. Most 'the model is broken' conversations end the moment you see how high a one-line rule already sets the bar.
Exploratory data analysis before anything else
We look at your data before we make any modeling decisions. Missing values, outliers, class imbalance, data leakage. We chart each feature's distribution, count how often it is null, and check whether the positive cases are 50% of your rows or 2% of them, because a 2% fraud rate means accuracy is a useless metric and a model that predicts 'never fraud' every time scores 98%. These are not problems to fix at the end, they are problems that determine whether the model is even buildable.
Feature engineering for your domain
The raw columns in your database are rarely the best inputs to a model. We construct features that reflect the actual drivers of your outcome: recency since the last order, frequency over a trailing window, ratios between spend and tenure, lagged values, rolling aggregates. Features the model can use that a domain expert would recognize as meaningful, computed only from information that exists before the moment of prediction.
The right algorithm for your task
Classification, regression, or time-series forecasting each have different requirements. We select from gradient-boosted trees, random forests, linear models, and sequence models depending on what your data supports, not what sounds most impressive. A linear model you can read beats an ensemble you cannot when the relationship is simple, and gradient-boosted trees win when it is not. We let the holdout numbers settle that argument, not the brochure.
Holdout strategy that reflects production
We do not evaluate on randomly shuffled splits when your data has a time component. We train on the past and test on the future, the way the model will actually be used, so a sale in March never leaks into a prediction made in February. A model that looks accurate on a random split and fails on future data is a common and expensive mistake, and a random shuffle is the single most reliable way to fool yourself into shipping one.
Calibration and probability outputs
A model that says 80% chance of churn should be wrong about one time in five, not one time in fifty. Raw scores from many models are ranked correctly but numerically off, so a 0.8 might really behave like a 0.6. We check this with a reliability curve and correct it, because the instant someone sets a threshold or multiplies a score by a dollar amount, an uncalibrated number quietly poisons every decision built on top of it.
"A gradient-boosted tree with the right features built in a day will outperform a neural network trained for a week on the same problem. Choose the tool that fits the data, not the one that impresses in a pitch."
What you get
A prediction your team can act on.
Not a Jupyter notebook that lives on one person's laptop. A pipeline your team can run, understand, and retrain when new data arrives.
- Exploratory data analysis with data quality findings and recommendations
- Feature engineering pipeline on your actual data sources
- Trained model with performance metrics on a held-out test set
- Calibrated probability outputs where applicable
- Inference pipeline: batch scoring or real-time API depending on your use case
- Documentation so your team can retrain with new data and understand what each feature does
Not sure if your data is clean enough to model? Tell us what you have and what you want to predict. We will tell you if it is buildable.
Invoke usIs this the right call
When this fits.
Good fit
- You have historical data with a label you want to predict: churn, fraud, demand, price
- The data is structured: rows and columns in a database or data warehouse
- A business decision depends on this prediction today and is being made manually or not at all
- You want to know which features actually drive the outcome, not just a black-box score
Wrong call
- Your data is images, text, or audio. That is a vision or language model problem, not a tabular one.
- You have fewer than a few thousand rows. With very small datasets, a simple rule-based system often outperforms any model.
- You need the model to explain every individual prediction in legal or regulatory terms. Interpretability requirements that strict need a different approach from the start.
Deployment and scale
Batch or real-time, wherever the decision lives.
Some predictions run on a schedule: score every customer overnight, generate next week's demand forecast on Sunday, flag suspicious transactions in a daily batch. Others need to happen at the moment a decision is made: approve this loan application now, show this user the right offer in this session. We build for whichever mode your business actually runs in.
The model ships as a container with a defined input schema. The same scoring logic runs in your data warehouse, behind an API, or inside your existing application without being rewritten for each environment.
When your data distribution shifts over time, the model scores will too. We wire up monitoring so you see when input distributions change and can decide whether to retrain before the model's performance degrades in production.
What we settle before we begin: what decision the model is feeding, how often it needs to run, and what the cost of a wrong prediction is. Everything else follows from those three.
Tell us the decision you are making by hand.
Tell us what you are predicting, what data you have, and what happens today when no model exists. We will tell you whether a predictive model can help and what it should be trained on.