Cursor AI Development Malta

We assess your team's current development workflows, technology stack, repository structure, and coding conventions. We establish baseline metrics for key productivity indicators — task completion times, PR cycle duration, bug rates — that will be used to measure the impact of Cursor adoption after implementation.

Our team configures Cursor for your specific environment: .cursorrules files encoding your conventions, project-level context for large repositories, model configuration for your typical task mix, and integration with your existing toolchain. Configuration is done in close collaboration with your senior engineers to capture institutional knowledge.

We deliver a structured half-day training session covering Cursor's core capabilities — tab completion, inline editing, Composer, chat, and codebase Q&A. Training uses your actual codebase so engineers immediately see how Cursor understands your specific code context rather than generic examples.

Beyond basic completion, we train teams on advanced Cursor workflows: multi-file refactoring, test generation, documentation, code review assistance, and debugging. We develop team-specific workflow playbooks documenting the highest-value AI-assisted patterns for your common development tasks.

We identify and develop internal Cursor champions within your Malta team — engineers who become expert users and internal advocates. Champions receive additional training and support, creating a self-sustaining learning culture that continues to develop AI coding capability without ongoing external training dependency.

At four to six weeks post-adoption, we review productivity metrics against baseline, gather team feedback on friction points, and refine configuration and workflows based on real usage patterns. This optimisation session captures learnings from actual usage and addresses any adoption challenges before they become entrenched habits.