Staff repeatedly handle similar information
Staff repeatedly sort, summarise, compare or rewrite similar material.
AI integration for web systems
AI features for specific work tasks
We add AI features for document processing, information retrieval, classification, summaries and drafting.
We start with one defined task, test the quality of the output and only then connect the feature to the system your team uses.
Discuss an AI integrationWhen AI can help
Start with the task, the data and the cost of an error
AI is useful when you can define the input, the expected output, who or what will use it and how an incorrect result will be detected.
Useful information is difficult to find
Documents and records exist, but finding the right answer takes too long.
Incoming data is unstructured
Messages, files or descriptions need to be classified, converted into data or routed to the right person.
Staff need a useful first draft
The system can prepare a response, summary or other draft while a person makes the final decision.
Connect the AI feature to the wider web system and its application architecture, so it can use the correct data, permissions and validation rules.
Examples of AI features
Specific improvements inside the existing system
Document data extraction
Identify required fields and document sections and turn them into structured data.
Classification and routing
Categorise incoming information and pass it to the correct process or member of staff.
Search across documents and records
Find answers within an approved collection of organisational documents or content.
Summaries
Prepare summaries of documents, conversations or records for a defined staff task.
Drafts and suggestions
Prepare replies, explanations or other working drafts while retaining human review.
Data enrichment
Identify and standardise properties in text or images when the result can be checked.
How we work
Test the value before committing to a large integration
- 01
Define the task and risk
Specify the input, expected output, users, sensitive data and the consequences of an incorrect result.
- 02
Test with representative examples
Use typical and difficult cases to determine whether the feature saves enough time to justify the cost.
- 03
Add appropriate controls
Set permissions, output checks, audit logs and a safe process for unavailable or unreliable results.
Outcome
An AI feature with a clear purpose and clear limits
You receive a tested AI feature whose output can be reviewed, checked and controlled.
Common questions
Frequently asked questions
Which AI model or provider will be used?
That depends on the task. We compare capability, data-handling terms, response time, cost, integration requirements and maintenance before selecting a provider.
Can the integration use non-public organisational data?
It can, but data sensitivity, provider terms, storage location, retention, access and redaction must be assessed first. No more information should be sent to an external model than the task requires.
Can AI output be trusted automatically?
Not in every case. The greater the consequence of an error, the stricter the checks must be. The workflow may require automated validation, human review or prevent AI from taking the final action.
Official guidance
If an AI feature processes personal data or may be subject to specific regulatory requirements, refer to the ICO guidance on AI and data protection and the European Commission's AI Act overview.
Want to test whether AI can save time?
Tell us what material is being processed, which result staff need and which errors cannot be accepted.