AI Integration

BACKGROUND

Predicto is a startup offering plug‑and‑play predictive maintenance industrial sensors.

When we joined, the platform was working but had been neglected: dependencies were outdated, bugs had accumulated, and performance had slipped. Our mandate was to modernize and stabilize the product, speed it up, and deliver new capabilities—most notably an AI feature where a user uploads a machine photo and the system identifies the machine and returns its key information.

TECH CHALLENGES

The core challenge was that the platform had become slow and buggy. We ran our Quality Criteria assessment and turned the findings into a focused renovation plan. Over the next iterations we fixed long‑standing defects and failing tests, tuned API and database performance, redesigned the UI for clarity and speed, and upgraded the stack to the latest Spring Boot and Angular versions.

TOP CHALLENGES WE SOLVED

• We restored stability and responsiveness without regressions.

• Modernized critical dependencies safely without regressions in the platform behaviour.

• Refreshed the UI/UX around operator workflows to reduce user friction.

• Pushed the test suite to a reliable green state.

• Modernized the system in parallel with active development, ensuring an on-time, feature-heavy production launch.

OUR SKILLS IN ACTION

We began by conducting a comprehensive audit of Predicto's existing codebase and infrastructure. Our quality assessment revealed critical areas for improvement including outdated dependencies, performance bottlenecks, and system instabilities. We developed a detailed renovation roadmap that prioritized immediate fixes while planning for long-term scalability.

Our team systematically addressed each issue, starting with critical bug fixes and security updates. We upgraded the entire Spring Boot infrastructure to the latest version, ensuring better performance and security. The Angular frontend was completely modernized, providing users with a responsive and intuitive interface.