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Part of Data & AI

Applied AI & automation

Put machine learning and automation to work on specific operational problems, with measurable value and people kept in control.

Useful AI starts with a costly, repetitive problem, not with a model. We pick use cases where the data exists and the gain is clear, then build them into the processes people already use.

When to call us. Signs it’s time for this work.

  • Teams re-key the same information between systems every day.
  • Forecasts for demand, cash or stock rely on guesswork.
  • Fraud, churn or credit risk is spotted too late.
  • AI pilots impressed in demos but never reached production.

What’s included. Scoped to your context, delivered end to end.

  1. 01

    Use-case discovery and prioritisation

    A shortlist of AI and automation opportunities ranked by value, feasibility, data readiness and risk.

  2. 02

    Predictive models

    Forecasting, scoring and anomaly detection built on your data and tested against real operating conditions.

  3. 03

    Process automation

    Workflow and robotic process automation for rule-based tasks, combined with AI where judgement is needed.

  4. 04

    Deployment and MLOps

    Monitoring, retraining and controls so models keep performing once they leave the pilot stage.

How we work. Four phases, with value at every step.

  1. 1

    2–3 weeks

    Discover

    Workshops with operational teams to surface candidate use cases and check the data behind each one.

  2. 2

    4–6 weeks

    Prove

    Build a working prototype on real data and measure it against the current way of working.

  3. 3

    6–10 weeks

    Industrialise

    Integrate into systems and workflows, add monitoring and train the teams who will rely on it.

  4. 4

    Ongoing

    Improve

    Track performance, retrain when data shifts and move on to the next use case on the list.

What you walk away with

  • Prioritised use-case portfolio
  • Business case per use case
  • Validated prototype
  • Production model or automation
  • Monitoring and retraining plan

What changes

  • Staff time moved from re-keying to judgement.
  • Earlier warning on risk, fraud and demand.
  • AI that lasts beyond the pilot.

Common questions.

Is our data good enough for AI?

Often it is good enough for a first use case. We check during discovery and say plainly when data work must come first.

Will automation replace our staff?

Our aim is to remove repetitive work, not people. We plan role changes and training with you, and design processes where people make the final call.

Do we need our own data scientists?

Not at first. We build and run the initial solutions, then help you decide which skills to hire or develop to sustain them.

Discuss Applied AI & automation

Tell us where you are. We’ll tell you honestly whether and how we can help.

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