Production of the materials for the PUMA Future online campaign

Our LegacyApp case study describes application maintenance in an AI-assisted development model. It is an example of how AI can support software work, rather than a promise of the same results for every project. Review the project in our portfolio and tell us about your existing application, codebase and maintenance needs so we can assess a suitable approach.

We tailor automation and AI integrations to the process, data and existing systems of a particular organization. We start by identifying a repeatable task, required integrations and a way to evaluate the result. A pilot helps assess usefulness before a wider rollout. Industry-specific requirements and the role of human review are agreed for the project.

We integrate AI into business workflows and existing systems. Depending on the project, this may include processing text or documents, assisting repetitive tasks and connecting AI services with business tools. We select the approach after reviewing the process, available data and required integrations, then test a pilot before a wider rollout.

Before implementing AI, we agree on the intended use, data access and the role of human review. A pilot helps identify inaccurate outputs and limitations in the actual workflow. Privacy, security and industry requirements need to be assessed for the particular project and AI service. Important decisions should have appropriate human oversight.

We start with a specific business process and the task AI should support. We review the data and systems involved, agree on a pilot and define how its results will be assessed. Integration, testing, human review and maintenance are planned for the project. Wider implementation depends on what the pilot demonstrates.