Rolling out AI across a large organisation is quite different from adding a single AI feature to one application. It involves strategy, platform, governance, vendor mix and the people who will have to work with it. We help enterprise organisations get it fully up and running: pragmatically, with working pilots in production rather than a report.
By enterprise AI implementation we mean rolling out AI as a capability across a large organisation: not as a single standalone AI feature, but as a shared layer that multiple business units, teams and use cases can draw on. It touches strategy, platform engineering, governance, vendor management and workforce all at once. At organisations of roughly five hundred employees or more, with a complex IT landscape and several business units, it quickly becomes a situation where every department starts something with AI in parallel. The work then is no longer about choosing a tool, but about bringing those initiatives together under one architecture, one governance layer and one vendor strategy.
The typical enterprise has by now launched several AI pilots in parallel. The board has issued a mandate, a few teams have built something with copilots, marketing has bought a GPT tool and the customer service department has brought in a chatbot vendor. The problem seldom lies in getting started; it lies in scaling up, getting governance in place and making those initiatives work together. Compliance pressure from the AI Act, DORA, NIS2, the MDR or sector regulators means that a board that once simply asked "what are we going to do with AI?" now also asks for risk classification, audit logs and model cards.
We combine a technically thorough AI strategy with the actual build: think of a central LLM platform, RAG infrastructure, evaluation tooling and a vendor-neutral architecture in which Anthropic, OpenAI, Azure OpenAI and open-source models can run side by side. Not a 200-page Big Four report, but working pilots in production and a platform your teams can build on. We are also not a reseller of any particular cloud AI service: none of our model choices are tied to kickbacks, so our advice on which provider fits each use case can be given honestly.
The target audience for this type of engagement is enterprise CIOs and CTOs with a board-level AI mandate, organisations with several parallel AI pilots that fail to scale, post-merger situations where two AI strategies need to come together, financial institutions under DNB or AFM supervision, healthcare organisations with overlap between MDR and GDPR, government bodies that must register in the algorithm register, and enterprise B2B SaaS vendors that want to offer AI as a feature to their own customers. The engagement looks different in each of these contexts, but the underlying layers (strategy, platform, governance, workforce) recur every time.
The CIO/CTO must demonstrate within a year that AI delivers structural value, not just in a PowerPoint but in a few concrete business cases running in production.
Five parallel AI experiments have sprung up across different business units. None of them is in production, and nobody knows which ones should continue and which should not.
After a merger or acquisition, two organisations each have their own AI vendor, copilot rollout and data landscape. These need to be converged or deliberately kept apart.
Financial institutions under DNB/AFM, healthcare providers subject to MDR and GDPR, or government bodies under the algorithm register obligation: governance needs to be in place before the first audit.
The organisation is tied to a single LLM provider for all use cases and wants more flexibility: model routing per use case, fallback models and better cost control.
Enterprise B2B SaaS vendors who want to build AI into their own product for their customers: not just internal use, but AI as a capability of the platform itself.
Value mapping, use-case portfolio, gating criteria, risk classification per use case under the AI Act, model cards, audit logs and a human-in-the-loop policy. This is where our AI strategy discovery comes in.
An internal AI platform layer: an LLM gateway with model routing, a prompt library, an evaluation framework, a retrieval layer for RAG, observability and cost tracking. Teams can build their own AI agents and use cases on top of it without reinventing the wheel each time.
AI succeeds or fails with the people who use it. AI literacy at every organisational level (board, engineering, operations, compliance, HR), plus change management for the teams whose work is actually changing.
What we typically build, advise on and set up with you. Not every programme needs all nine; the combination follows from the discovery.
Value mapping per business unit, prioritisation, ROI framework, no-go criteria.
LLM gateway, prompt library, evaluation suite, RAG infrastructure, observability, cost tracking.
AI Act mapping, bias monitoring, model cards, audit logs, human-in-the-loop policy, data residency.
Pilots per business unit, gating criteria, go/no-go decisions, sunsetting of what doesn't work.
Multi-LLM strategy, contract management, cost monitoring, fallback paths between models.
Prompt injection defence, output filtering, PII leak prevention, model access control, secret handling.
AI Act Articles 4 and 9-15, GDPR overlap, DORA, NIS2, MDR for healthcare, algorithm register.
AI literacy training for each level: board, engineering, operations, compliance, HR and end users.
What changes for people, who loses tasks, who takes on new responsibilities, and how to communicate that without causing panic.
Six phases that are not necessarily sequential: strategy and governance run continuously, while the platform and pilots are built in parallel.
The first sprints. We map the existing AI landscape: which pilots are running, which vendor contracts are in place, and which data landscape sits underneath. We identify the use cases with the greatest leverage and build a gating framework based on impact, feasibility, risk and learning value. The output is a workable roadmap, not a report.
An LLM gateway, evaluation framework, RAG infrastructure, observability and cost tracking as a shared layer. We work vendor-neutrally: Anthropic, OpenAI, Azure and open-source models all run on the same abstraction. Adding a new use case means a configuration change rather than another vendor evaluation. Links to our custom LLM integrations.
Three to five pilots running at once across different business units, all judged against the same gating criteria. That covers rule-based assistants as well as generative AI solutions, retrieval-augmented systems and autonomous agents where they fit. Not everything makes it to production, and that is by design rather than failure. Pilots that don't make the cut still deliver value: you learn what doesn't work far sooner, without a department having spent a year investing in it.
Pilots that pass gating receive production hardening: monitoring, fallback models across vendors, cost control per use case, security review, user onboarding and SLO definitions. From this point it becomes software engineering rather than experimentation. The platform from phase 02 already handles much of this, so a new production rollout is largely use-case-specific work.
Alongside the pilots, we deliver AI literacy training tailored to each layer of the organisation. The board needs something different from the engineering teams, and HR something different again from the compliance department. We also provide change management for departments where the work genuinely changes: which tasks disappear, which shift, and how to communicate that without sparking panic or cynicism.
Governance is not a one-off project. We put audit logs, model cards and risk reviews on a fixed rhythm. Every new use case is automatically classified under the EU AI Act. As models change, vendor pricing shifts and regulation tightens, the governance layer is adjusted without the platform or the pilots built on top having to be rebuilt.
A half-hour introductory conversation in which we go through the AI initiatives you already have under way, where you are running into scaling or governance problems, and whether a structured programme is the right next step.
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