Applied artificial intelligence
Artificial intelligence that supports decisions you can verify.
At a glance
- Searchable, organised documents
- Internal assistants whose answers cite their sources
- Supervised administrative automation
- Analytics and operational support
The need
A lot of useful information sits in documents, records and data that nobody has time to read or cross-check. Artificial intelligence (AI) can help, if it is applied to a specific problem, evaluated on real data and controlled by people.
Intended outcome
Clearly defined use cases, evaluated on the client's data and built into processes, with clear human responsibilities.
Scope
- Identifying and prioritising use cases, with the expected outcome and the risk of each.
- Data preparation and quality assessment.
- Classification, extraction and search in controlled environments.
- Knowledge assistants whose answers are linked to their sources (RAG, retrieval-augmented generation), where the case justifies it.
- Evaluation before going live and monitoring afterwards.
- Defining the human boundary: what the AI suggests, who signs it off and what is recorded.
Deliverables
- A use-case sheet: problem, data, expected outcome, risks and human control.
- An evaluation report with agreed metrics and known limitations.
- A solution built into the process, logging suggestions and sign-offs.
- A monitoring and periodic review plan.
Deployment options
On premises, private cloud, public cloud or hybrid, depending on how sensitive the data is. Models are chosen on requirements, evaluation and portability, not brand.
Limits and dependencies
- AI suggests and assists; high-impact actions require human sign-off and named owners.
- We do not promise perfect accuracy or error-free results. Cost savings are only claimed with results measured in the specific case.
- Third-party models and services remain subject to their licences; we do not transfer ownership of what is not ours.
- Client data is only used within the scope and environments defined in the contract.
Continuity and exit
Reversibility deliverables for this service, set out in the contract and updated before exit:
- Data and export formats
- Evaluation data and logs of suggestions and sign-offs in open formats.
- Documentation
- Each use-case sheet, the evaluation report and monitoring procedures.
- Configurations
- Configurations and instructions of the solution, under version control. Third-party models are identified with their licence; whether they can be handed over depends on that licence.
- Responsibilities
- Who signs off each type of suggestion and who is accountable for the results.
- Licences and contracts
- Regularisation of licences for third-party models and services and of data-use conditions; whatever those licences allow is transferred.
- Knowledge transfer
- Training teams in the critical use and review of AI suggestions.
Next step
Describe a task that takes up time and the outcome you want; we will assess whether AI is the right answer.
Technical detail
Information for technical and procurement teams. Open each topic to read it.
The human boundary
For each use case it is written down what the AI may do on its own, what it only suggests and what requires a human decision. Suggestions and decisions are logged, so that it can be audited who decided and on what information.
Evaluate before going live
The solution is tested with representative client data and agreed metrics before it goes live. Results and known limitations are recorded in a report. A simulation is never presented as a working product.
Where the models run
Location is a design choice, not a guarantee. Beyond location, access, keys, logs, external dependencies and exit conditions are all defined.