Applied Model Development
Models built against the business problem, not the benchmark.
Applied model development is mostly not modeling. It is deciding what to predict, assembling data that supports the decision, establishing a baseline, and then — only then — improving on it with methods that are measured and reproducible. That is how we work.
The shape of an engagement
- Problem framing. What decision does the model inform, what does an error cost in each direction, and what operating point does that imply? This conversation regularly changes the target metric — and sometimes the whole approach.
- Dataset curation. Auditing labels, hunting leakage, designing splits along the natural grouping unit, and deciding what additional data is worth collecting before more modeling is.
- Baselines. A simple model, honestly evaluated, that every later run must beat. For vision and time-series work this is often a frozen pretrained backbone with a linear head.
- Modeling. Transfer learning and fine-tuning where pretrained weights exist; careful architecture work where they do not. One change at a time, fixed seeds, recorded runs.
- Handover. Training scripts your team can rerun, documented hyperparameter decisions, and an evaluation report a stakeholder can read.
Domains
We work mainly in computer vision (classification, detection, segmentation), time-series (forecasting, anomaly detection), and structured/tabular problems — the applied workloads of industrial, healthcare, retail, and agtech teams.
Development engagements are hourly, as a self-contained project or embedded with your engineers. Either way the work product is reproducible: anyone on your team should be able to retrain the model from the repository and get the reported numbers.