Build: Agentic & Generative Systems
Predictive & Decision Intelligence
Classical machine learning where it beats LLMs: forecasting, anomaly detection, optimisation, and pricing.
8–12 weeks Milestone-gated T&M
Not every decision needs a language model. For forecasting, anomaly detection, optimisation, and pricing, well-engineered classical ML is cheaper, faster, and more explainable. We build the models, the MLOps pipeline, and the decision dashboards — and we tell you when an LLM is the wrong tool.
Who it's for
- Supply-chain, finance, and revenue leaders with numerical decisions at scale
- Data-science teams that need a production path for their models
- Executives who want explainable decisions
What you get
Production models with monitoring
MLOps pipeline (training, validation, deployment, drift detection)
Decision dashboards and playbooks
How it works
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01
Decision framing
What decision, what cost of error, what baseline.
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02
Model
Feature engineering, model selection, backtesting.
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03
Productionise
Pipeline, monitoring, and retraining cadence.
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04
Adopt
Dashboards and decision rights for the people who act on it.
Questions
Yes. Sometimes the answer is a rule, a report, or a process fix. We would rather lose a project than ship a model that does not beat the baseline.