Services

From the first question to a system your team runs.

We work on four kinds of problem, and most engagements touch more than one. Each starts with how your organisation really works, and each ends with your team able to run, judge and improve what we built together.

StrategyGovernanceAI in your workflowsDigital delivery and dataWho we work with

  1. Where should AI go?

    Digital transformation and AI strategy

    The situation

    You know AI and data could change how your organisation works, but there are countless places to start, and every vendor has a different answer. You need to know where it will make a real difference, and in what order.

    What we do

    • Map how work really flows today: the people, processes, systems, data and decisions involved
    • Find the points where AI, automation or better data will change outcomes, and the points where they won't
    • Design the target architecture: what connects to what, where data lives, which tools fit
    • Set out a roadmap in steps your organisation can take, each one useful on its own

    What changes

    You know where AI will pay off first, what it depends on, and what to do next, in steps your organisation can actually take.

    A first step: a review of one workflow or one part of the business. You get a map of how it works today, the opportunities ranked, and a plain answer on what is worth doing.

  2. How do we stay in control?

    AI and data governance

    The situation

    People are already using AI tools, on their own terms. Information sits in many systems and nobody is sure which version is current. You want the benefits of AI without the risks of using it blind.

    What we do

    • Policies and rules for AI use: where it may help, who reviews its output, what is recorded
    • Roles and decision rights, so everyone knows who decides what
    • Data and information standards, and a shared environment for project information
    • Risk reviews of AI systems and the controls around them
    • Governance of digital delivery on construction projects, across owner, managers, designers and contractors

    What changes

    AI moves from scattered experiments to something the organisation uses openly and safely. Decisions move faster because the rules are clear, and you can show anyone how a result was reached.

    A first step: a governance health check. You get a clear picture of how AI and data are used today, the main risks, and the first rules and roles to put in place.

    Example: governing a project's digital delivery

  3. Build it into our work

    AI in your workflows, and AI-native software

    The situation

    The real gains are inside the work itself: the design calculation repeated on every project, the report compiled every week, the question answered again and again. Off-the-shelf tools rarely fit how your team actually does it.

    What we do

    • Turn engineering design-calculation and review processes into software that runs end to end
    • Bring AI into project management: tracking, reporting, document control and follow-up
    • Build assistants that answer from your own documents and cite their sources
    • Automate routine data and document work, with people approving the decisions
    • Design and build AI-native applications: software where AI is part of the design from the start, with fixed, tested steps wherever a result must be exact

    What changes

    Your experts spend their time on judgement, not on repetition. Methods that lived in one person's spreadsheet become tools the whole team can use, check and improve.

    A first step: a working pilot on one of your own workflows, so you judge it by using it, with a plan for taking it into daily use.

    Example: RailDyn · Example: UTT admissions assistant

  4. Digital delivery and data

    BIM, model quality and project data

    The situation

    Models and project data are only valuable if people can trust them. Too often, checks are manual, information is incomplete, and the data that could guide decisions is locked in files nobody can query.

    What we do

    • Model checking against your information requirements, visible in 3D and repeatable on every model
    • Information requirements and standards for BIM delivery
    • Project data connected across models, documents and systems, ready for analysis and AI
    • Plain-language questions over models and project data, answered from the data itself

    What changes

    Owners can see what was checked before they accept a model. Teams find problems early, and project data becomes something you can ask questions of.

    A first step: a check of one model against your requirements. You get the findings laid out in 3D and a set of rules you can reuse.

    Example: BIM model review

Who we work with

Organisations where specialist knowledge, complex projects and good decisions matter.

  • Owners, developers and contractors

    Complex projects with many parties, where information, approvals and reporting need to work as one system.

  • Engineering and design firms

    Teams whose expertise lives in calculations, reviews and models, and who want it to scale without losing rigour.

  • Infrastructure and public organisations

    Bodies that manage assets and programmes over many years, and need data and governance they can rely on.

  • Knowledge-led organisations

    Universities, professional services and any organisation where expertise is the product and documents hold the know-how.

Your team's capability is part of the work

We don't hand over a black box. Every engagement includes working sessions and training, so your people understand how the system works, can judge its output, and can keep improving it after we step back.

We are not tied to any one AI provider. We choose the models and tools that fit your needs, your data and your rules, and we can run them in your own environment where that matters.

Not sure where to start?

Tell us what you're trying to change. We'll suggest a first step that is useful on its own.

Talk to us