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AI & Data Science Delivery

We build models around a defined operational decision: what is likely to fail, where risk is increasing, what demand to expect, or which cases need attention. An engagement can cover the path from data assessment and model development through validation, explanation, deployment, and handover, depending on what the team needs.

What this covers

  • Predictive and risk models across tabular, geospatial, text, and sensor data
  • Time-series and geospatial forecasting, with uncertainty reported explicitly
  • Clustering, anomaly detection, and recommender systems when labels are sparse or absent
  • Data preparation and integration, including record linkage when source systems do not align
  • Validation and explanation designed around the decision, audience, and regulatory context
  • Production deployment, documentation, monitoring, and handover when in scope

Where this work tends to start

These engagements start with a decision, not a preferred algorithm. We establish what needs to be predicted or improved, what action will follow the output, and how success will be measured.

Then we assess the data as it exists — its coverage, quality, timing, labels, and whether the sources can be combined reliably. Sometimes combining those sources is the first phase of the work; in other cases the harder problem is sparse outcomes, sensor noise, geospatial variation, or changing conditions over time.

What a project needs to start

A decision or measurable question agreed before modeling starts: what the team needs to know, what action the output should support, and how usefulness will be judged. If the available data cannot support that, we say so before a budget is committed.

Explanation sized to the decision. How much a model needs to explain depends on the decision it supports, the audience reading the output, and the regulatory context — an analyst debugging a score needs more than an operator triaging a queue, and a public-sector model needs to hold up in technical and executive review.

How we report accuracy

Two models can report the same accuracy and behave very differently in production. So we report calibration alongside it: whether events given a seventy percent score happen about seventy percent of the time.

Reliability diagram comparing an overconfident model with a calibrated onePredicted probability on the horizontal axis against observed frequency on the vertical. A perfectly calibrated model follows the diagonal. The overconfident model sits well below it: the things it calls 95 percent likely happen about 62 percent of the time. The calibrated model tracks the diagonal closely, so a 70 percent prediction from it happens roughly seven times in ten. Both models can score the same accuracy; only the calibrated one's probabilities mean what they say. The curves show the shape of each behavior, not data from an engagement.PERFECTLY CALIBRATEDsays 95%, happens 62%010PREDICTED PROBABILITY1OBSERVED FREQUENCYBoth models score the same accuracy; only the calibrated one's probabilities mean what they say.
overconfidentcalibratedperfectIf you only need a ranking — who to call first — calibration barely matters. If you're going to act on the number itself — stage crews against it, order a part against it — seventy percent has to mean seven times in ten. An accuracy score can't tell these two models apart, which is why we report calibration alongside it. The curves show the shape of each behavior, not data from an engagement.

Deployment and handover

What a production engagement defines

When production deployment is in scope, these are agreed before delivery — so the system has an operator, an owner, and a maintenance path from day one.

Operating environment
Where the system runs — your infrastructure and credentials, agreed at scoping.
Ownership and source
Who owns the source code, training pipeline, and environment configuration, and what transfers at handover.
Documentation and runbook
What operating documentation and runbook the receiving team gets, and who maintains it.
Monitoring
Monitoring on inputs and outputs, when production operation is in scope.
Retraining responsibilities
Who retrains, on what schedule or trigger, and with what data — agreed before handover.
Scope varies by engagement: an analysis engagement may end at documented findings, while a production engagement includes the full checklist above.

Discuss your data and decision

Bring the decision you need better evidence for. We'll scope what your data can support before you commit to a project.

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