Services

Machine learning services that reach production.

Three focused ways we help: building the pipelines that make models run reliably, deciding where machine learning is worth using, and fixing models that are already in production.

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Pipeline engineering

A model only creates value once it runs reliably, repeatedly, and with someone accountable for it. We build the workflows that make that possible.

Most teams do not need more modelling. They need the path from data to decision to be repeatable: the same inputs producing the same outputs, changes verified before they reach users, and failures visible before they become incidents.

We work inside your codebase and your cloud, replacing fragile scripts and manual steps with pipelines your team can read, test, and change. Where something already works, we leave it alone.

What's included

  • Data ingestion with explicit contracts, schema checks, and quality gates
  • Reproducible training runs: versioned data, code, configuration, and artifacts
  • Automated evaluation and regression tests before anything ships
  • Deployment with packaging, environment parity, rollout, and rollback
  • Monitoring for drift, input quality, latency, cost, and failures
  • Runbooks, documentation, and alerts your team can act on

Data model strategy

Deciding where machine learning belongs — and where it does not. Before committing budget, it is worth knowing which problems are genuinely solvable with the data you have.

We start from decisions, not algorithms. Which decisions are slow, expensive, or inconsistent today? What data already exists around them? What would a better decision be worth?

From there we test feasibility on your real data, agree on how success will be measured, and produce a plan that is honest about effort, dependencies, and risk.

What's included

  • Use-case workshops that connect AI ambitions to concrete decisions
  • Data readiness audit: coverage, quality, labelling, access, and privacy limits
  • Feasibility work on a sample of your real data
  • Baselines and success metrics agreed before any model is built
  • A phased roadmap with dependencies, risks, and expected outcomes
  • A build-versus-buy assessment, including off-the-shelf options

Model improvement

When a model underperforms in production, the cause is usually the data, the evaluation, or the way the system is used — not the choice of algorithm.

We treat it as a diagnosis. We look at where the model fails, why the offline numbers disagreed with reality, and which constraint — data quality, evaluation design, leakage, latency, or cost — is actually holding performance back.

You get a prioritised list of fixes with their expected impact, and hands-on help implementing the ones you choose.

What's included

  • Audit of data, features, evaluation, and deployment assumptions
  • Error analysis on real failures, sliced by segment and edge case
  • Evaluation repair: leakage, train and serve skew, missing baselines
  • Data quality work: labelling, coverage, deduplication, feedback loops
  • Latency, throughput, and inference cost optimisation
  • A retraining and drift strategy with documented triggers
How we work

How an engagement runs.

No open-ended discovery and no surprises. Every engagement follows the same four steps, and you always know what is being decided and why.

  1. Intro call

    We talk through the problem, the data you have, and the constraints you work under. You leave with an honest first read on whether machine learning is worth pursuing.

  2. Technical discovery

    A focused review of data sources, systems, evaluation, and delivery constraints — ending in findings rather than documents for their own sake.

  3. Written plan

    Scope, phases, deliverables, responsibilities, and risks, agreed before implementation starts. No open-ended commitments.

  4. Delivery and handover

    Hands-on work with your team, decisions documented as they happen, and a clean handover to whoever runs the system next.

Engagement types

Ways to work with us.

The right shape depends on what you already have and what you need to decide next. Most relationships start small and grow from there.

Focused review

A contained piece of work — a model audit, an architecture review, or a feasibility question — that ends with findings, priorities, and a recommendation. Useful when you need senior judgement before spending more.

Delivery project

A defined outcome with agreed phases: a pipeline running in production, a model fixed, or a first system built alongside your team.

Ongoing support

Regular senior input for teams that already ship: reviewing work, unblocking engineers, and keeping an eye on models in production.

Next step

Tell us what you are trying to build.

A short conversation is usually enough to see whether we can help — and if we cannot, we will say so.

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