Services

Who this is for

Who this is for

• You already have several AI pilots or Shadow AI use cases, but:

  • no one has a complete picture,
  • you don’t know which ones to push to production first,
  • regulation (e.g. the AI Act) is looming, but there is no clear plan.
• Or you are just starting with AI and:

  • want to understand where AI can realistically help (operations, sales, back-office),
  • don’t want to start with an expensive, unfocused proof of concept,
  • need a short, sober assessment: “does this even make sense at our scale?”
• In both cases, you want a vendor‑neutral view that:

  • is not tied to a single platform,
  • tells you plainly what makes sense and what is just “AI theatre”.

What you get

• Interviews with key stakeholders across IT, Data, Risk, and the business.
• An assessment of your current state against a pragmatic Production OS maturity model:

  • Strategy & Outcomes,
  • Governance & Risk,
  • Process & Operating Model,
  • Architecture,
  • Run & Optimise.
• A clear Scorecard: where you are, where the gaps are, and what to tackle first.
• A 90‑day plan – 3–5 concrete moves that:

  • unlock the path to production,
  • reduce regulatory and operational risk,
  • are realistic in your context (budget, people, internal politics).

How it works in practice

• 2–3 weeks of collaboration, mostly remote.
• A handful of 60–90 minute sessions with key people + review of existing material.
• Final session: walk‑through of the Scorecard with your decision‑makers (CIO/CAIO + Risk + Business).

Who this is for

• After a Scorecard (or internal review) you already know:

  • which use cases are worth taking to production,
  • that the real pain is the gap between PoC and production.
• You have in‑house teams (IT/Data), but they lack:

  • patterns for controls‑as‑code and governance,
  • a reference Production OS architecture,
  • experience with regulated environments.

What you get

• Embedded support for a defined period (e.g. 8–12 weeks):

  • presence in key project meetings,
  • guidance on architectural decisions,
  • a “translator” between Business, IT, Data, and Risk.
• Co-design of:

  • a minimal AI Gateway (controls, logging, guardrails),
  • processes for submitting and approving AI use cases,
  • roles and responsibilities across the lifecycle of the solution.
• 1–2 use cases taken all the way to:

  • stable, monitored production,
  • a state you can show to regulators and auditors as a reference pattern.

How it works in practice

• Work in 1–2 week sprints, always with a clearly defined “next step”.
• The format adapts to your team:

  • part of the work is consultation and decision support,
  • part is co‑creating artefacts (diagrams, policies, playbooks).

Who this is for

• Organisations that:

  • want to scale AI beyond one‑off projects,
  • must show regulators and auditors a coherent set of controls,
  • don’t want to invent everything from scratch.
• Especially where:

  • there is already some form of data platform / MLOps / LLM platform,
  • but no unified governance model connected to what really happens in the systems.

What you get

• A reference Production OS architecture tailored to your organisation:

  • AI Gateway / proxy,
  • monitoring and observability,
  • registration and classification of AI use cases (registry),
  • integration with existing security and compliance systems.
• A policy‑to‑control map:

  • which rules apply,
  • where they are technically enforced (which component / process).
• A set of artefacts for:

  • the Board,
  • regulators (e.g. in the context of the AI Act),
  • internal and external audit.

How it works in practice

• A mix of workshops (with technical and risk teams) and conceptual work in the background.
• The outcome is a framework you can apply across future AI use cases,

  • grounded in your real stack and constraints, not in a vendor brochure.