Data Science & Machine Learning
Turn data you already have into models, forecasts, and dashboards that drive decisions.
Book a discovery callThe problem
What this solves
- ✕Data sits unused across disconnected systems.
- ✕No in-house ML capability to prototype ideas.
- ✕Manual analysis can’t spot patterns or anomalies.
- ✕Leadership wants forecasts, not spreadsheets.
What we deliver
Tangible outputs
- ✓Predictive models (churn, demand, risk, forecasting).
- ✓Anomaly and fraud detection prototypes.
- ✓Clean, reproducible data pipelines.
- ✓Dashboards that turn models into decisions.
- ✓An honest read on what your data can and can’t support.
Tech stack
Tools and platforms
Process
How the work flows
Audit
Assess data quality, availability, and the real question.
Design
Frame the problem and define a measurable target.
Build
Prototype, evaluate honestly, and iterate.
Handover
Ship a usable prototype with documented assumptions.
Pricing model
Ways to engage
Data audit
Assess feasibility before you invest.
Prototype build
A working proof-of-concept model.
Monthly retainer
Iterate toward production ML.
FAQ
Common questions
Our data is messy, can you still help?
Messy data is the norm. A large part of the work is cleaning and framing, and we’ll be honest early about what’s feasible.
Do you deploy to production?
We focus on prototyping and validation first, then help productionise on AWS when the prototype earns it.
What proves you can do this?
3rd place out of 251 in the Nedbank Machine Learning & Data Science Challenge 2026, plus an AWS AI Practitioner certification.
How long does a prototype take?
Typically a few weeks, depending on data readiness. The audit tells us before committing.
Ready to talk?
Book a free 15-minute intro call. We'll scope whether this is the right fit. No pressure.