Machine learning models, trained on your data.
When a general-purpose model is not accurate enough, we train one that is. We start from strong foundation models or proven classical methods, fine-tune on your labeled data, and hand over a model you own, with the evaluation to prove it works.
Example: a radio-frequency signal classifier that identifies emitter types from raw IQ samples, reaching about 97% accuracy across eight classes and clearly beating a hand-built rules baseline.
Built around your data
Sensor streams, documents, images, transactions, or time series. We design features and architectures around the data you actually have.
Measured, not promised
Leakage-safe splits, held-out test sets, confusion matrices, and accuracy across operating conditions, reviewed with your team.
Ready to deploy
Exported for your environment, from cloud APIs to edge hardware, with documentation and a retraining path.
How it works
- 1
Scope and data audit
We define the target, check label quality, and agree on how success is measured.
- 2
Baseline
A simple model or rules baseline sets the bar every later model has to beat.
- 3
Train and fine-tune
We iterate on features, architectures, and data, tracking each run.
- 4
Evaluate and hand over
You get the model, the evaluation report, and the pipeline to retrain it.
Related reading
Custom AI vs off-the-shelf tools: how to decide
Build versus buy is really a choice between four options. Here is how to tell which one fits a given workflow, and when a custom build earns its cost.
Why AI pilots fail, and how to get yours into production
Most AI pilots stall for reasons that have little to do with the technology. Here is what the research actually says, the causes that come up again and again, and how to design a pilot that is built to ship.
An AI implementation roadmap: from first use case to production in nine weeks
Five phases, each with a clear goal, a short list of deliverables and a decision at the end. Here is how a focused first AI project gets from idea to daily use in about nine weeks.