Bespoke models when off-the-shelf LLMs don't fit — fraud detection, anomaly scoring, domain classifiers. Training pipelines, honest eval harnesses, and reproducible performance reporting.
LLMs are great at "summarize this" and "answer this". They are expensive and unreliable at "predict which of these transactions is fraud", "cluster our customer base by behavior", or "score this anomaly against eighteen months of history". Bespoke ML still wins on structured, tabular, and domain-specific problems — if you can find a team that builds it honestly. Most ML projects die not because the model is wrong but because the eval was optimistic.
Nothing exotic on this list — but every failed ML project we've read the post-mortem on skipped at least one of these. We don't.
// ML delivery coverage — every scope
// skip one and the model ships worse than the baseline.
From problem framing to drift monitoring in production. We work on your infrastructure with your data — we don't take copies.
We look at the data before we look at the models. What's the distribution, what are the splits, where's the leakage, what are the labels actually measuring. Output is a problem framing document and a realistic performance ceiling. If the ceiling is below your business need, we tell you before anyone writes training code.
Simpler-first: rule-based baseline, then logistic regression, then tree ensembles, and only then deep models when they actually win. Every jump justified by eval, not vibes. We build training pipelines that are reproducible: anyone on your team can retrain and get the same numbers six months later.
Deployment with versioning and rollback. Drift detection on inputs and outputs. Performance monitoring against a holdout that matches production distribution. Retrain triggers — manual, scheduled, or drift-driven — with runbooks your team can execute without us.
Free initial scoping — 30 minutes to tell you whether your problem is ML-shaped, what an honest baseline would look like, and whether custom ML is worth the build.