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AI Engineering

We engineer AI around the analytical workflow: model structures, data sources, scripts, review gates and the people responsible for the result. Automation should shorten the path to evidence without hiding how the evidence was made.

The planning question

Where should AI shorten the analytical chain, and where must it stop?

We start with the work as it exists. That may mean helping a modeller assemble a scenario, interrogate large result sets, trace an assumption to its source, or turn a meeting record into decisions and owned actions. Existing tools remain the foundation when they already work.

AI can misread an incomplete instruction or produce a plausible explanation from the wrong data. Consequential changes therefore require explicit permissions, validation and human accept, edit or reject decisions. We design those controls as part of the system, not as an afterthought.

Experience

Built inside real analytical workflows.

01

Model-aware assistants

We have designed assistants that map natural-language requests to model classes, objects, properties and ordered actions. The useful output is not only a changed file; it is a traceable plan that a modeller can inspect, validate and reverse.

02

Analytical retrieval and explanation

Our prototypes connect questions to large, multi-sector time-series outputs and model relationships. They gather the relevant variables, expose bottlenecks and present a reasoned explanation for review by an energy specialist.

03

Knowledge and workflow systems

We have scoped transcript ingestion, semantic retrieval, action extraction and project-status workflows, alongside orchestration of existing modelling scripts. Provenance, access control, versioning, error handling and rollback remain visible throughout.

How we work

A controlled path from question to evidence.

  1. 01Observe the workflow

    Map the current tools, handoffs, decisions, failure points and access rules.

  2. 02Choose a narrow task

    Prioritise one repeatable burden with a clear reviewer and acceptance test.

  3. 03Build a governed pilot

    Connect the minimum data and tools, with logs, permissions and review gates.

  4. 04Evaluate and hand over

    Test against expert judgement, document limits and transfer operational ownership.

What the team receives

Useful after the presentation ends.

  1. 01Current-state workflow and technical architecture
  2. 02Controlled prototype connected to approved data and tools
  3. 03Evaluation set, review gates and audit trail
  4. 04Documentation, handover and a decision on the next phase

Partnerships & consortiums

The strongest work has more than one perspective.

We are open to partnerships and consortiums with system operators, public bodies, research organisations, infrastructure developers and technology teams.

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