Sciematics
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Portfolios

A wider sample of shipped work, each with the metric it was judged on. For the full model cards, read the case studies.

Forecasting

SKU demand forecasting

Beat a seasonal naive baseline by 3.5 points of MAPE.

MAPE 8.4% CI 7.6 to 9.2
Forecasting

Warehouse capacity planning

Peak day staffing planned from a distribution, not a point estimate.

Coverage 94% at 90% target
Forecasting

Delivery ETA prediction

Reports its own uncertainty, so dispatch knows when to distrust it.

MAE 11 min CI 10 to 12
Vision

Surface defect detection

Annotation protocol rebuilt before a single model was retrained.

Recall 0.94 CI 0.91 to 0.96
Vision

Packaging label verification

Edge deployment at 22 ms per unit.

Precision 0.98 CI 0.97 to 0.99
Vision

Component counting from tray images

Replaced a manual count at end of shift.

Error 0.4% CI 0.3 to 0.6
Language

Loan document extraction

Abstains rather than invents. The abstention rate is published.

Field accuracy 98.1%
Language

Support ticket triage

Routing beats the rules engine it replaced, on the classes that matter.

Macro F1 0.87 CI 0.85 to 0.89
Language

Policy document assistant

Retrieval with mandatory citation. No fine tune.

Hallucination rate 1.2% measured
Platform

Feature store and training pipeline

Reproducible runs, versioned data, rollback in minutes.

Training reproducible to seed
Platform

Drift monitoring rollout

Alerted on a genuine distribution shift six weeks after launch.

Detected shift at day 43
Assurance

Independent evaluation of a vendor model

Reproduced the vendor's claim, then evaluated on unseen data.

Vendor claim not replicated

No work listed in this discipline yet.

Confidentiality

Why the last entry says the claim was not replicated

Because it was not. A client asked us to evaluate a model they had been sold. We reproduced the vendor's reported metric on the vendor's own split, then evaluated on data the vendor had never seen. The performance did not hold.

We list that engagement here because a portfolio containing only successes is not a portfolio, it is an advertisement. The most valuable thing we sold that client was a number they did not want.

Ask for a reference

Tell us the shape of your problem and we will introduce you to a client who had one like it, including one we advised not to proceed.

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Next step

Send 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.