Use cases
AI in context.
See how research, machine learning and agentic systems could address practical problems in different sectors.
Illustrative examples. These scenarios describe possible engagements, not completed client projects or achieved results. Feasibility, data access and evaluation would be agreed for each project.
Find a sector
Public sector & local government
Explore better services with transparency and human oversight built in.
Resident enquiry triage
- Engagement
- Proof of concept → MVP
- The challenge
- Council teams spend time reading and routing enquiries about services such as housing repairs, waste and benefits.
- A possible approach
- A small, adapted language model could classify enquiries, extract case details and draft replies for officers to approve. Vulnerability and safeguarding concerns would follow an explicit escalation route to a person.
- Potential deliverables
- A prototype tested on past enquiries, a routing evaluation, data protection assessment input and a draft transparency record, with case-system integration scoped for an MVP.
- How we would evaluate it
- Compare routing decisions with experienced officers, measure time to the correct team and assess escalation failures on an agreed test set.
Civil society & charities
Help limited resources go further while protecting the people you support.
From beneficiary feedback to impact evidence
- Engagement
- Discovery → Proof of concept
- The challenge
- Survey comments and service feedback can contain useful evidence that is difficult to review at scale.
- A possible approach
- A pipeline could minimise or redact personal information before proposing themes for staff to check. Findings would link to controlled source records so reviewers can inspect the evidence.
- Potential deliverables
- A reviewed theme map, suitable example comments, a trend dashboard and a reusable reporting pipeline with data-handling notes.
- How we would evaluate it
- Compare proposed themes with staff coding, track review time and assess whether the resulting evidence is useful for reporting.
Health & care
Evaluate AI against local needs, populations and clinical review processes.
Local validation of a clinical AI tool
- Engagement
- Independent validation
- The challenge
- A supplier’s published results may not show how a tool performs on a particular organisation’s patients or workflow.
- A possible approach
- An evaluation would be agreed with the clinical team and safety lead, using suitable retrospective local data. Testing could cover calibration, subgroup performance, failure cases and a plan for monitoring drift.
- Potential deliverables
- An evaluation protocol, local performance report and evidence for the organisation’s clinical safety and supplier-assurance reviews.
- How we would evaluate it
- Provide evidence for a go, conditional go or no-go decision. Clinical safety decisions and approval remain with the responsible clinical team.
Defence & security
Assess dependable AI under constrained compute, power and connectivity.
Efficient sensor processing at the edge
- Engagement
- Research proof of concept
- The challenge
- Sensor models that work in the cloud may exceed the power, compute or bandwidth available on a device.
- A possible approach
- Candidate models could be adapted, distilled and quantised, then benchmarked on target hardware. The study would examine the trade-offs between task quality, latency, power and transmitted data.
- Potential deliverables
- A device benchmark, an optimised candidate model and a technical report explaining the trade-offs and next-phase options.
- How we would evaluate it
- Compare task performance with an agreed baseline and measure latency, power and bandwidth against the platform’s operating limits.
Energy, oil & gas
Use operational data to investigate reliability and maintenance decisions.
Predictive maintenance for rotating equipment
- Engagement
- Proof of concept → MVP
- The challenge
- Threshold alarms on pumps and compressors can be noisy, while useful early signs of failure remain hidden in sensor data.
- A possible approach
- Signal processing and anomaly detection could identify patterns in vibration, temperature and pressure. Models would be evaluated on historical failures, with engineers reviewing alerts and their usefulness.
- Potential deliverables
- A data quality review, a benchmarked detection model and an alert interface, with on-site deployment and feedback scoped for an MVP.
- How we would evaluate it
- Measure warning time on known failures, false-alarm rates and engineer feedback before judging operational value in a pilot.
Logistics & supply chain
Give planners better evidence for decisions across the network.
Demand forecasting for warehouse and fleet planning
- Engagement
- Proof of concept → MVP
- The challenge
- Spreadsheet forecasts can miss demand peaks, leaving planners to respond with overtime, agency staff or late deliveries.
- A possible approach
- Forecasting could combine order history, seasonality and other available demand signals. Forecast ranges and explanations would help planners understand uncertainty rather than rely on a single number.
- Potential deliverables
- A model benchmarked against the current method, a planner dashboard and a documented process for updating and monitoring forecasts.
- How we would evaluate it
- Compare forecast errors with the existing baseline and track staffing and delivery measures during an agreed pilot.
Telecommunications
Investigate network operations and efficient signal processing, from data to hardware.
Network root-cause analysis agent
- Engagement
- Proof of concept → MVP
- The challenge
- One fault can trigger many related alarms across systems, making the underlying cause hard to identify quickly.
- A possible approach
- An agent with read-only access could group alarms and inspect topology, recent changes and performance data. It would propose a cause with supporting evidence; engineers would approve any network change.
- Potential deliverables
- An agent prototype, structured diagnostic steps and an evaluation based on replayed incidents, with approval and audit requirements documented.
- How we would evaluate it
- Measure time to identify a cause on historical incidents, the quality of ranked suggestions and the number of alarms requiring manual review.
Agent-assisted signal-processing research
- Engagement
- Research proof of concept
- The challenge
- Neural approaches to equalisation or digital predistortion can perform well but be too complex for line-rate hardware.
- A possible approach
- Within an engineer-defined search space, an agent could propose and test architectures against measured link data. Candidate designs would be assessed on both signal performance and implementation cost.
- Potential deliverables
- A search framework, a shortlist of candidate architectures, complexity analysis and a technical report for hardware review.
- How we would evaluate it
- Compare link performance at equal or lower complexity, operations per symbol and the work needed to reach a hardware-ready design.
Retail
Improve product information and service workflows with review at the right points.
Product catalogue enrichment agent
- Engagement
- Proof of concept
- The challenge
- Incomplete supplier information creates manual work before product listings can go live.
- A possible approach
- An agent could extract attributes and draft copy from supplier material, checking claims against evidence. Unverified details would go to a merchandiser. Safety and allergen information would come from approved sources and require human checking.
- Potential deliverables
- An enrichment pipeline, claim-checking rules, a review queue and an evaluation against approved listings, with model adaptation considered where useful.
- How we would evaluate it
- Measure attribute completeness, review effort and time to publish, while tracking unsupported claims and safety-critical errors.
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