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services/ai & data/ml & analytics
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ML & Analytics

Models and dashboards that change what people do. We build analytics tied to decisions, and machine learning only where it beats a simpler answer.

A model that is not wired into a decision is a research project. Before building anything we establish what action the output will trigger, who takes it, and what accuracy that action actually requires, which is frequently lower than assumed, and occasionally far higher.


A great deal of what gets scoped as machine learning is better served by a well-designed query, a threshold, or a clearly presented dashboard. Those are cheaper to build, easier to explain, and do not degrade quietly as the world shifts. We reach for a model when the problem genuinely needs one.


When it does, we treat it as production software rather than a notebook: versioned data and models, evaluation against a held-out set, monitoring for drift, and a defined path for retraining. Otherwise the accuracy you measured at launch is the last honest number you get.

What this includes

  • /Analytics and dashboards tied to specific decisions
  • /Predictive and classification models where warranted
  • /LLM applications, RAG, extraction, summarisation
  • /Evaluation frameworks and accuracy baselines
  • /Model deployment, monitoring, and drift detection
  • /Retraining pipelines and versioning

When teams call us

  • /Dashboards nobody opens because they change nothing
  • /Manual judgement work at a volume people cannot sustain
  • /Unstructured documents or text nobody has time to read
  • /A model in a notebook that never reached production
  • /Predictions degrading since launch with no monitoring
// other ai & data services

Vibecoded to Production

Your AI built it. We make it safe to run. We take AI-generated prototypes that already work and turn them into software you can put in front of customers.

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AI Discovery Workshops

Find the AI features worth building, and the ones that are not. A few structured days that replace a backlog of speculative ideas with a short, costed roadmap.

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Data Engineering

Pipelines you can trust the numbers from. We build the infrastructure that turns scattered operational data into something a business can actually make decisions on.

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Common questions

What does ML & Analytics include?+

We build analytics, dashboards, predictive models, classification systems, and LLM-based applications where they clearly improve a business decision or workflow.

How do you decide whether machine learning is actually needed?+

We start with the decision the output needs to support and compare ML against simpler baselines such as rules, queries, or thresholds. If the simpler approach performs well enough, that is what we recommend.

Can you take an existing model from a notebook into production?+

Yes. We can productionize existing models with versioning, deployment, evaluation, monitoring, drift detection, and retraining workflows.

Do you build LLM applications as well?+

Yes. We work on use cases such as retrieval-augmented generation, extraction, summarisation, classification, and other LLM-powered workflows.

How do you measure whether a model is good enough to ship?+

We define evaluation criteria and compare the model against a baseline using held-out or representative data before it reaches production.

Do you monitor models after launch?+

Yes. We can track model quality, drift, failures, and usage over time, with defined processes for retraining or changing the model when performance degrades.

Need help with ml & analytics?

Whether it is a rebuild or extra hands, the first call is 30 minutes.

Book a 30-min call