Problem framing and baselines
A simple baseline established first, so model value is measured rather than assumed.
Models on your data
Machine Learning Development
Predictive and classification models trained on your data, evaluated honestly and deployed as services.
What this is
Not every problem needs a language model. Where you have historical data and a repeating prediction such as churn, demand, risk or classification, a trained model is usually cheaper, faster and more accurate. We build these properly: baseline first, honest validation, and a deployed service with monitoring for drift.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- Forecasting runs on spreadsheets and intuition, and misses consistently.
- A data science notebook produced a promising model that never shipped.
- Reported model accuracy collapsed in production, and nobody saw it coming.
- Classification is done manually at a volume that no longer scales.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- Problem framing with a measurable target and an honest baseline to beat
- Feature pipeline built from your data sources, reproducible end to end
- Trained model with validation that reflects real deployment conditions
- Deployed inference service with defined latency and throughput characteristics
- Monitoring for data drift, prediction drift and performance decay
- Retraining process, schedule and rollback path
- Documented limitations and the cases where the model should not be trusted
Capabilities
What the system does
Feature engineering
Reproducible pipelines with leakage checks and point-in-time correctness.
Model development
Classical methods preferred where they suffice; deep learning where the data justifies it.
Honest validation
Time-based or grouped splits matching production reality, not random splits that flatter results.
Deployment
Batch or real-time inference services with versioning and rollback.
Drift monitoring and retraining
Input and output distributions watched, with alerting and a defined retraining trigger.
Applied
Where it is used
Each of these is a bounded job with a measurable result, which is how we prefer to scope a first build.
01
Demand and capacity forecasting
Predicts volume by period and segment to inform staffing and stock decisions.
02
Churn and retention scoring
Flags accounts at risk early enough for intervention, with the drivers shown.
03
Lead and opportunity scoring
Ranks inbound interest on historical conversion patterns rather than instinct.
04
Anomaly and fraud detection
Surfaces transactions and records that deviate from established patterns.
05
Pricing and recommendation
Supports pricing and cross-sell decisions from observed behaviour.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
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01
Data sources
Transactional databases, event logs, warehouse tables and third-party feeds.
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02
Feature pipeline
Extraction, aggregation, point-in-time joins and validation, versioned and reproducible.
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03
Training
Experiment tracking, hyperparameter search and validation under production-like conditions.
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04
Model registry
Versioned artefacts with the metrics and data snapshot behind each one.
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05
Serving
Batch scoring jobs or a real-time inference API, with caching where appropriate.
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06
Monitoring
Drift detection, performance tracking against outcomes, alerting and retraining triggers.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- Documented limitations and explicit out-of-scope cases for every model.
- Validation designed to avoid leakage and over-optimistic reporting.
- Feature review for proxies that could produce unfair outcomes in people-affecting decisions.
- Human decision ownership retained where a prediction affects an individual materially.
- Model and data versioning so any prediction can be reproduced and explained.
Delivery
Our approach
How we run the engagement
Discovery
Process walkthroughs, data review, constraints and a written scope you sign off before build.
Architecture
Data model, integration boundaries, hosting and the security model, agreed up front.
Proof of concept
A narrow build on your real data against an agreed quality bar, so the go-ahead is evidence-based.
Development
Two-week increments, each ending in a working deployment and a review.
Testing
Automated coverage on critical paths, evaluation sets for AI behaviour, acceptance testing with your users.
Deployment
Staged rollout into your infrastructure with monitoring and a tested rollback path.
Monitoring & support
Quality, cost and latency tracking after launch, with handover documentation or an ongoing retainer.
Proven work
Products we have built and sold
We are product builders as well as a services team. These are our own commercial platforms, not client references.
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Questions
Machine Learning Development — FAQs
The questions we are asked most often about this service.
How much data do we need?
It depends on the problem, but a useful rule is thousands of labelled examples with reasonable class balance for classification, and two to three years of history for seasonal forecasting. We assess your data before committing to a scope.
Should we use a language model instead?
For prediction over structured historical data, usually not: a trained model is cheaper, faster and more accurate. Language models suit unstructured text and reasoning tasks. We often use both in one system.
What accuracy can you achieve?
We will not quote a number before seeing your data. We establish a baseline, report honest validation figures, and you decide whether the lift justifies the build.
What happens when the model degrades?
Drift monitoring alerts on input and prediction distribution shifts and on measured performance against outcomes. Retraining is a defined, scheduled process with a rollback path.
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