Most organisations do not have an AI problem. They have a sequencing problem. There is enthusiasm, a scattering of pilots, a licensing bill, and no agreed view of which use cases are worth pursuing, what the governance obligations are, or what has to be true before anything reaches production.
We work with government agencies and regulated businesses where a wrong answer has consequences. That constraint changes what a useful AI strategy looks like.
Why AI strategies stall
They stall in three predictable places. The pilot works but cannot be deployed because nobody established who owns the risk. The use case list is generated from what the technology can do rather than from where the organisation actually loses time. Or the data underneath is not in a state that supports the ambition, and that only becomes clear after the business case is approved.
In regulated environments a fourth failure appears: the organisation cannot explain, after the fact, how a decision was reached. That is fatal wherever an outcome is reviewable.
What we do
- Readiness assessment. Where your data, security posture and information governance actually sit against what you want to do. This includes a Microsoft Purview-led baseline, because sensitivity labelling and access governance determine what Copilot and agents can safely see.
- Use case pipeline. A prioritised set of opportunities scored on value, feasibility and risk, documented to the level a delivery team can pick up. On a recent engagement with a commercial law firm this produced fourteen business requirement documents and a sequenced agentic AI pipeline.
- Governance model. Who approves an agent, what it is permitted to access, how its actions are logged, how long records are kept, and what happens when it gets something wrong.
- Roadmap and operating model. Sequencing, capability required, build versus buy, and how the organisation runs this once we leave.
Governance is the strategy
In most sectors governance is a constraint on the strategy. In regulated environments it is the strategy, because it determines what you are able to deploy at all.
Three controls do most of the work. Scope every agent to the permissions of the person using it, so it cannot surface anything they could not already access. Log every interaction and every action for audit, and decide retention before launch rather than after the first complaint. And name a content owner accountable for the currency of the grounding sources, because an agent is only ever as good as the material behind it.
We are also clear about where AI does not belong. Eligibility decisions, enforcement outcomes and anything reviewable are the wrong use case, not because the technology cannot produce an answer but because the answer has to be explainable, consistent and defensible on appeal.
From strategy to delivery
We are a delivery firm, not an advisory house, which shapes the advice. A strategy we cannot build is a strategy we will not recommend. Our own work spans Copilot Studio agents in government, AI-assisted evidence processing for a prosecution authority, AI-assisted redaction through our Seers Redact product, and a headless CRM with an agentic experience layer where the interface is an agent rather than a form.
The data foundation matters as much as the model. We are partners with Microsoft, Databricks and Snowflake, so we build the data layer under an AI program on whichever platform fits, including where an organisation already runs more than one.
Delivery is ISO/IEC 27001 certified, on Microsoft, with sovereign hosting and security-cleared personnel available where the environment requires it.