Generative AI development for useful applications

Make the workflow useful before making it autonomous

A model demonstration is only one part of an application. Users still need the right information, understandable outputs and a way to deal with errors. Your team needs to know what the system can access, how it behaves when a dependency fails and who will maintain it.

We work on defined uses of generative AI within software: helping people find information, preparing summaries for review, classifying incoming material or assisting with a repetitive task. These are starting points for evaluation, not promises that every workflow should use AI.

If the business case or use case is still unclear, start with AI consulting and automation. This development service focuses on building and integrating a selected approach.

Agree the application and data boundaries

Before connecting a model to business systems, we work through the inputs, users and permitted actions with your team. The scope can include:

• Connecting approved information sources while preserving user access restrictions.
• Building the application interface and the APIs around the model.
• Showing source material where people need to check an answer.
• Adding review steps before an output changes a record or triggers an external action.
• Defining timeouts, failure responses and a route back to the existing process.

Model and provider choices depend on the task, data restrictions, response time and operating cost. We make those trade-offs explicit rather than assuming one tool fits every application.

Connect business data through APIs or MCP

We can expose approved data through APIs or the Model Context Protocol (MCP) so that an AI application can use it within an agreed scope. That starts with the existing permissions, the information a user should be able to retrieve and whether any action can change a record.

Read access and write actions need separate decisions. We design for authentication, restricted access and review before consequential changes, and evaluate how the integration handles unavailable tools or untrusted content. Connecting a source does not make every record appropriate for every user or task.

Evaluate the difficult examples

A useful trial includes incomplete inputs, contradictory information and requests the system should decline or pass to a person. We agree representative examples and acceptance criteria before treating a successful demonstration as evidence for release.

The evaluation should consider the work people still need to do. An answer that looks convincing but takes longer to check may make the process worse. Quality, review effort, latency and cost all belong in the decision.

An internal example: Watchkeeper

Our Watchkeeper project uses an AI agent to review server evidence for our own security checks. The operating system supplies the facts; the agent highlights changes and prepares summaries. People investigate findings and approve changes to the expected baseline.

That separation of evidence, interpretation and authority is part of the engineering design. Watchkeeper is an internal tool, not a claim that AI replaces security specialists or establishes compliance on its own.

Plan for operation after the trial

Production work includes monitoring, versioned configuration, evaluation after changes and a named owner for incidents and improvements. We agree how your team will operate the integration and where ongoing support is needed.

Our technical assurance page explains the wider engineering and security practices behind this work. If your application instead needs to consume predictions from an existing model or specialist team, see machine learning integration.