Integrating AI into your product without losing control

Choose a task, not a chatbot-shaped brief

An AI feature is easier to evaluate when its job is specific. Finding an answer in approved documentation, extracting fields from a document or preparing a draft response are clearer starting points than asking a model to run the business.

Define the current workflow, where people lose time and what a successful result looks like. Some tasks are better handled by ordinary rules or search. AI is useful when its capabilities fit the problem, not merely because it can be added to a screen.

• Knowledge search with links back to source material.
• Document classification and extraction with validation.
• Drafting and summarisation that a person can review.
• Assisted workflows with approval before important actions.

Keep permissions outside the model

If a product serves several businesses, an assistant must not retrieve another customer's records. Filter retrieval using the signed-in user's permissions before data reaches the model. Do not rely on a prompt telling it to keep information private.

Apply the same principle to tools. The application should check access and validate arguments whenever a model requests an action. A model-generated instruction is not authorisation to change a record, send a payment or contact a customer.

Treat retrieved content as untrusted

A document, web page or user message can contain instructions designed to redirect an AI system. This is commonly called prompt injection. Connecting an assistant to more data and tools increases the importance of clear boundaries.

Limit which tools are available, use narrow schemas and separate reading from writing. Require confirmation for sensitive actions. These controls reduce risk, but no single prompt or filter eliminates it. Test hostile inputs as well as the happy path.

Map the data journey before launch

List the information sent to the provider, why it is needed, how long it is retained and where it is processed. Check provider terms and configuration rather than assuming every AI service handles data the same way. Minimise personal information and avoid logging sensitive prompts by default.

For UK projects, consider the relevant data protection obligations and whether a data protection impact assessment is needed. The answer depends on the use case, not just whether the feature uses AI. Digirar's own UK-based data protection lawyer is part of the in-house team, bringing privacy questions into the product conversation early. This article is general engineering guidance, not legal advice.

Evaluate realistic outcomes, including failure

Build a representative set of tasks with expected outcomes before connecting the feature to live workflows. Include missing information, conflicting documents, outdated answers and attempts to access restricted records. Review both the answer and any proposed action.

For a knowledge assistant, useful measures might include answer correctness, source relevance and how often it admits it cannot answer. For document extraction, measure field accuracy and the volume of manual correction. A confident response is not evidence that it is correct.

Budget for latency, cost and recovery

AI calls can be slow, unavailable or more expensive than expected. Set timeouts, usage limits and sensible retry policies. Give visitors a clear failure state and a non-AI path where possible. Cache only where data freshness and privacy allow it.

Roll out to a limited workflow, review real results and expand when the evidence supports it. At Digirar, we integrate AI into products and solutions alongside the surrounding engineering: permissions, interface design, monitoring and ongoing testing. The goal is a useful product capability, not a demo that becomes unpredictable under real use.