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Applied artificial intelligence
AI applications and workflows engineered for practical business use
We help organisations apply AI to product features, business knowledge and operational processes. Our work combines model integration, data access, workflow design and software engineering to develop solutions that can be evaluated against a defined purpose.
What notice do customers need to give to change a corporate booking?
Knowledge assistant
Changes are accepted up to 14 days before the booking date. Later changes are credited to a future booking and need a manager’s approval.Booking terms · p.4Corporate policy · §2.1
Ask about policies, documents or procedures…
Knowledge
Answers with sources
Controls
Review · Audit logs
Applications
Where
AI
can
make
a
practical
contribution
The value of an AI solution depends on the task it supports, the information available to it and the way it fits into the wider workflow. We establish these requirements before choosing models or designing the implementation.
Searching internal knowledge
Knowledge
Answers drawn from your own documents, with source references.
Extracting information
Documents
Classification and extraction of data from documents, forms and correspondence.
Drafting
Responses
Draft replies and summaries prepared for a person to review and send.
Coordinating tasks
Agents
Agents that interact with defined tools and services, with approval stages where needed.
Our approach
From an opportunity to an evaluated application
Potential applications include searching internal knowledge, extracting information from documents, preparing responses and coordinating tasks across connected tools. The appropriate approach depends on the quality of the available data, the consequences of errors and the level of human oversight required.
01
Use-case assessment
We define the task, review the available information and agree criteria for assessing output quality and practical value.
02
Prototyping and evaluation
We test models and implementation options against representative examples, examining accuracy, response time, cost and limitations.
03
Engineering and integration
We connect the selected approach to the relevant application, knowledge sources and business systems, with access controls and review steps suited to the workflow.
04
Monitoring and refinement
We establish how performance will be assessed after deployment and use the findings to guide changes to prompts, retrieval, models and workflow design.
Quality and oversight
Designed for the possibility of error
We define when a result requires verification or escalation, and how performance will be assessed after deployment.
Evaluation datasets and quality checks
Source references and constrained workflows
Human review and escalation for uncertain results
Guardrails, personal-information filtering and audit logs
Usage, response-time and operating-cost monitoring
Scope of services
From model selection to monitoring
AI features for web and mobile applications
Model integration and selection
Knowledge assistants using approved business content
Document extraction, classification and processing
Agents that interact with defined tools and services
Workflow orchestration and approval stages
Prompt libraries and retrieval-augmented generation using business content
FAQ
Common questions
If your question is not covered here, we will be glad to discuss it.
Assess where AI could make a practical contribution to your business.
Share the task, process or product you have in mind so we can consider the data, integration and evaluation requirements.
Can you fine-tune or privately deploy a model?
We can assess fine-tuning, local models and private deployment where the use case, data and operating requirements justify them. This assessment also considers whether prompt design or retrieval from business content would provide a suitable solution.
How do you identify a suitable AI use case?
We look for a defined task, accessible information and a practical way to assess the output. We also consider how errors would affect users and what review process is appropriate.
Which models do you work with?
We assess hosted and open-source options against the requirements of the task, including output quality, deployment needs and cost. The selection follows evaluation of representative examples.
Can AI use our company’s documents and knowledge?
We can develop retrieval-based applications that use approved content as context for responses. The scope includes how information is prepared, accessed and kept current.
How do you address inaccurate outputs?
Evaluation, source references, constrained workflows and human review can reduce and manage errors. The design should account for the possibility of inaccurate output and define when a result requires verification or escalation.
How are data access and operating costs handled?
We review data flows, provider arrangements and access requirements during planning. Expected usage informs the design of monitoring, limits and operating-cost controls.