Services/ML engineering
Capability
ML engineering
Classical ML when it beats a generic LLM for the job.
Feature pipelines, training, inference, and monitoring for prediction, ranking, anomaly detection, and scoring systems that need to stay accurate over time.
What we build
The engineering pieces included under this capability.
Problem fit
Choose ML vs LLM vs rules based on data, latency, and risk.
Pipelines
Features, training jobs, and reproducible model artefacts.
Inference
Online or batch scoring with clear SLAs.
Monitoring
Drift, quality, and cost signals after launch.
Retraining
Update paths when data or behaviour shifts.
Example use cases
Concrete jobs this capability is built for. Not a full project proposal.
01
Lead or risk scoring
Score leads, applications, or accounts from your historical data so teams prioritise the right ones first.
02
Anomaly detection
Flag unusual transactions or ops events in near real time for a human to review.
03
Demand forecasting
Predict demand or capacity from past patterns and feed the numbers into planning workflows.
Build path
- 01
Baseline
Data availability, label quality, and success metric.
- 02
Model path
Train, validate, and compare against a simple baseline.
- 03
Serve & watch
Deploy inference, monitor drift, plan retrain.
Typical stack
ML
Pipelines
Serving
Need this in production?
Bring the use case and constraints. We will say if a sprint or pilot fits.