Opportunity & feasibility
Define the task, baseline, available data and constraints. Consider conventional software or simpler automation alongside AI.
AI & intelligent automation
Practical AI. A defined problem. A measurable test.
Explore predictive models, language-based workflows and automation with a clear view of feasibility, evaluation and operational cost.
Discuss your requirementsWhen this can help
Recognise the challenge?
- A repetitive workflow consumes time that could be spent on higher-value work.
- You want to test an AI use case before committing to a production rollout.
- You need to understand whether available data can support a useful predictive model.
Potential scope
What an engagement can cover.
The combination depends on your requirements; scope is agreed before delivery.
Prototypes & evaluation
Build a bounded test around agreed criteria such as accuracy, failure modes, cost and the level of human review needed.
Workflow integration
Connect an appropriate solution to the systems and approval steps people already use.
Production planning
Identify monitoring, access, data handling and operating responsibilities before progressing beyond a pilot.
Tangible outputs
Know what you’re working towards.
Specify the deliverables and acceptance criteria that matter for your engagement.
- A use-case definition with assumptions and evaluation criteria
- A scoped prototype or automation workflow
- Evaluation findings, limitations and a recommendation on next steps
- Integration and operating requirements for any proposed rollout
What we need to consider.
AI outputs can be incomplete or incorrect. Sensitive use cases require proportionate evaluation, human oversight and a clear decision about what the system is allowed to do. A successful prototype is not the same as a production-ready service.
How to start.
Describe the workflow, who performs it, its current cost or effort and what a useful improvement would look like. We can then discuss data availability and a suitable first evaluation.
More about the delivery approachUseful to know
Your questions, answered.
Do we need to know which model to use?
No. Start with the task and its constraints. Model and platform choices follow the requirements, including privacy, cost and integration needs.
Can you guarantee a particular accuracy or saving?
Outcomes depend on the task, data and operating environment. Agree evaluation criteria and test a representative sample before making a production commitment.
Can a pilot become a production system?
Potentially. A separate decision should consider evaluation results, security requirements, integration, operating cost and ongoing ownership.