Models that survive contact with reality.
Sciematics builds machine learning systems that are measured honestly, monitored after launch, and abandoned when the evidence says they should be. Most of what is sold as AI would not pass that test.
The uncomfortable first question
Almost every client arrives certain they need a model. A large share of them do not. Their data has leakage, or too few labels, or the decision it would inform is already made by a rule that works.
So we ask whether the data can support a model before we ask what the model should do. Nine times we have answered no and said so. That number is on this page deliberately.
The field has an evaluation problem
A model that reports 94 percent accuracy on a slide has told you almost nothing. On what test set. Against what baseline. With what uncertainty. Measured when.
Those four questions eliminate most of what is presented as artificial intelligence in a commercial setting. A classifier that beats a coin flip is impressive to nobody, and a classifier evaluated on data it was trained on is not evaluated at all.
We publish confidence intervals, name our baselines, and hold out a test set that no one sees until the end. This is ordinary scientific practice. It is unusual enough in industry that it has become our position.
Read the methodEight disciplines
Data Readiness Assessment
Before any model, an honest audit of whether your data can carry one.
Predictive Modelling and Forecasting
Demand, churn, risk, and price. Models judged against a baseline you agree beforehand.
Language and Document Systems
Retrieval, extraction, and assistants over your own documents. Rarely a fine tune, almost never a new model.
Computer Vision
Detection, segmentation, and quality inspection on real imagery, with the lighting you actually have.
Machine Learning Engineering
Taking a notebook that works once and making it a system that works every day.
Data Engineering and Platform
Warehouses, pipelines, and the unglamorous foundation everything above it depends on.
Analytics and Decision Support
Sometimes the answer is a query and a chart. We will say so.
AI Governance and Assurance
Independent evaluation of models, including ones we did not build.
Results, with their uncertainty
A model that affects a person deserves scrutiny
If a system decides who gets a loan, a diagnosis, or a job interview, then accuracy is not the only number that matters. Who does it fail, and how often, and can a human overturn it.
We evaluate across subgroups, document known limitations in a model card, and design the human oversight before deployment rather than after an incident.
Our governance practiceFour commitments we can be held to
What clients ask first
Usually whether their data is good enough. It is the right question, and it is rarely asked early enough.
Ask us directlySend us your data problem. We will tell you whether it is one.
A working data scientist reads every enquiry. You will get an honest read on whether your data can support the model you have in mind, before anyone quotes you a number.