ML & Data Engineering
A general-purpose model knows the average of the internet. Your advantage sits in what only you have: your customers' behaviour, your product's signals, your operational history. We build the pipelines that make that data usable and the models that turn it into decisions, so the business runs on intelligence it owns rather than on a statistical average of everyone else's.
Your data is a moat only if you can actually use it.
Most companies are sitting on years of purchase history, usage patterns, support tickets, and transactions that never inform a single decision. We build the engineering layer that cleans, structures, and pipes that data into models that predict churn, rank leads, optimise bidding, personalise at scale, and surface the signal your team is currently walking past. The same features that train the model feed every other capability we build, from the software your team runs on to the agents that act on your behalf, so the whole system gets sharper on data only you have.
Data engineering and pipelines
ELT pipelines, warehouse design, and real-time event streams built for machine learning rather than retrofitted to it. Clean, versioned, and documented, so models train on something that reflects your business instead of the accidents of how your systems were wired.
Custom ML models
Classification, regression, recommendation, and forecasting, built and tuned on your data. We deliberately scope the least complex model that solves the problem, because complexity is a cost you pay every time it needs retraining or explaining.
ML in production
Deployment, monitoring, and retraining pipelines. Models are instrumented so you know when they drift, retraining is automated where the data volume supports it, and a human review gate stays wherever the stakes make one worth the delay.
Feature reuse across the stack
The features that predict churn are frequently the ones that should decide which lifecycle email fires, which ad audience a customer joins, and what the product surfaces on their next visit. We build the feature layer once and let marketing, automation, and product all read from it.
- Data audit and lineage map
- ELT pipeline + data warehouse schema
- Trained and validated ML model with an evaluation report
- Model serving infrastructure (API or batch)
- Drift monitoring + retraining trigger setup
- Feature store documentation and ownership handoff
The engagement
Assess
Audit the data sources, test whether the signal you need is actually present, and state the ML problem in business terms with a number attached.
Pipeline
Build the extraction, transformation, and feature engineering layer first. A model on unreliable data is a very expensive coin toss.
Train
Train, validate against a holdout set, and compare against the dumbest baseline that could work. If the model cannot beat it, we say so.
Retrain
Monitor for drift in production, retrain on new data, and revisit the feature set as the business changes underneath the model.
Common questions
It depends on the problem. Recommendation systems typically want tens of thousands of events; a classification model can start on a few hundred labelled examples when the signal is strong. We test whether the signal exists before we scope a build, and we tell you when it does not.
Not necessarily. We build for maintainability and document the system for a single technical owner. For higher-velocity use cases we can run retraining and monitoring for you under an ongoing arrangement.
Then ship the rule. We benchmark every model against a trivial baseline, and there is no version of this where we recommend a model you do not need in order to bill for one.