Applied AI & ML Development
- Home
- Applied AI & ML Development
Models built for your constraints, not for a benchmark.
A model that scores well on a held-out split is not a system. Getting from one to the other means the unglamorous work: reliable data access, a training pipeline someone else can run, an evaluation harness that reflects the actual decision being made, and a deployment path through your own security review. That is the work we do.
We build inside your cloud tenancy, behind your identity provider, under your retention rules. Every model ships with a model card describing what it does, where it degrades and who signed it off — and with monitoring your team can read without our help.
What the engagement covers
A typical build runs eight to sixteen weeks, depending on data access and integration surface. Enablement runs alongside it rather than afterwards.
- Data access and quality assessment
- Versioned, reproducible training pipeline
- Evaluation harness tied to the real decision
- Deployment inside your own environment
- Drift detection and scheduled retraining
- Runbooks, model card and a trained owner
We embed with your team rather than working around them. Your engineers sit in our design reviews, your domain experts label and adjudicate the evaluation set, and your operations leads decide where the confidence thresholds sit. By the time we hand over, the people running the system helped build it.
What clients get out of it
The point of the work is not the model. It is what the model lets your people stop doing, and what it lets them start deciding with better information.
Fewer Manual Touches
Typically 40–60% of repetitive handling removed from the process.
Faster Cycle Times
Case turnaround measured in hours where it used to run in days.
Monitoring & On-Call
Drift alerting and a shared on-call rota for the first ninety days.
How we work with your team
Machine learning covers a wide span of techniques, and most of the value in commercial work comes from the less exotic end of it: gradient-boosted trees over tabular operational data, retrieval over your own documents, time-series forecasting with honest uncertainty. We reach for large language models where they genuinely earn their cost, and we say so when they do not. What matters more than architecture is whether the output lands in front of the right person, in a form they can act on, with enough context to know when to override it.