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AI Agents & Workflow Automation

Most AI projects fail somewhere between the demo and the deployment. We work on the part that decides it: choosing the right model for the job, grounding it in your own data, measuring whether the answers are actually good, and controlling what happens when they are not.

Capabilities

What this covers

The named disciplines inside this practice. An engagement usually needs two or three of them.

  • Machine learning

    Forecasting, classification, anomaly detection and recommendation — trained on your data and monitored for drift once live.

  • Generative AI

    Retrieval-augmented systems over private knowledge bases, with citation, evaluation harnesses and prompt versioning.

  • AI agents

    Tool-using systems that complete multi-step work, with approval gates, audit trails and hard limits on what they can touch.

  • Model operations

    Provider routing and failover, cost and latency budgets, offline evaluation sets, and regression tests that run in CI.

Deliverables

What you get

Artefacts, not status reports. Each of these is something you keep, and something your team can act on after we have gone.

  • Feasibility assessment with a measured baseline
  • Evaluation harness and quality benchmark
  • Production deployment with monitoring and cost controls
  • Model and prompt versioning strategy

Sectors

Where this practice is applied most

  • Healthcare

    Clinical education, health data integration and AI held to a safety standard.

  • Education

    Adaptive learning, assessment integrity and analytics educators can act on.

  • Finance

    Regulated platforms, risk tooling and AI with an audit trail attached.

  • Manufacturing

    Connected plant floors, predictive maintenance and operator interfaces that work in gloves.

Also relevant

Related services

Talk to us about ai agents & workflow automation

Tell us what you are trying to build and what is in the way. We will tell you whether we are the right people for it.

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