AI consulting and automation for your organisation

Start with the problem, not the model

AI is most useful when it improves a defined decision, workflow or user experience. A convincing demonstration is not enough to establish whether it will work in your organisation. The quality of the data, the consequences of mistakes and the effort of operating the solution all matter.

Our consulting work starts with the process you want to improve: who does the work, which systems it involves and where the delay or difficulty occurs. We consider conventional software and rules-based automation alongside AI. The recommendation should follow the problem, including when AI would add unnecessary complexity.

Assess an opportunity before investing in a build

An initial assessment can explore:

• Business value: the task to improve, its current cost or effort, and the outcome that would justify a change.
• Data readiness: the information a solution would need, its quality and the permissions governing its use.
• Risk and oversight: what could go wrong, how errors would be detected and which decisions must stay with a person.
• Technical fit: how a solution would connect to existing applications, access controls and operating processes.
• Evaluation: the representative examples and success criteria needed to judge a trial.

This helps decision-makers assess the investment and gives technical teams a basis for challenging assumptions. Where uncertainty remains, a focused prototype can test it before the scope expands.

Our AI readiness guide sets out questions your team can work through before that conversation, from choosing one process to deciding what evidence would justify a trial.

Test with representative data

A trial should use representative data and include situations in which the proposed solution might fail. We consider the quality of its outputs, the human effort required to review them, response times and running costs.

The result may justify further development, a narrower use case or a different approach. Agreeing the criteria in advance helps your team make that decision on evidence.

Move from prototype to controlled production

Successful trials still need engineering work before people can rely on them. Our approach considers data boundaries, model and provider selection, failure handling, human review, audit records and ongoing monitoring.

We integrate the solution with the wider application and agree how it will be maintained. Changes to models, source data or business processes can affect behaviour, so evaluation and ownership need to continue after launch.

Practical examples of AI and automation

Watchkeeper: an internal AI tool with a limited role

We built Watchkeeper for our own server security evidence checks. It reviews reports against documented expectations, flags changes for human attention and produces summaries to support ISO 27001 audit preparation.

The operating system supplies the facts. The agent reviews the evidence; people remain responsible for investigating alerts, changing the server and approving updated baselines. Its role is limited to supporting those checks and keeping evidence for review.

Concordia: conventional automation for a repetitive workflow

For Concordia, we built a browser automation tool to transfer visa sponsorship applications into a legacy Home Office system and synchronise the issued numbers back. This was conventional automation rather than AI. It helped Concordia issue more than 3,500 Certificate of Sponsorship numbers in under two months without increasing its workforce.

The distinction matters when choosing an approach. A repeatable workflow may benefit from straightforward automation, while an AI component needs a specific reason to be included.

Choose the right AI or automation service

For a selected language-model use case, explore generative AI development. To connect an existing prediction model to an application, see machine learning integration. For repeatable work with explicit rules, our RPA consulting service covers conventional automation and its operational needs.