• Matt B's team needed a new software tool, but received a quote for $125,000 by an outside vendor - far beyond their budget.
• Matt struggled to create a viable in-house solution using low-code tools, as none were sufficient to build a complete application.
• After discovering Pythagora, Matt built a fully functional app for his team in just two days.

Pythagora customer Matt B. works as a Senior Automation Engineer in the computational modeling department at a leading disease research institute. Recently, Matt's team was looking for a solution to enable the automated computers within the research institute’s hospital system to communicate with each other. After discussing solutions with several different software vendors, Matt's team was particularly frustrated when one vendor quoted them a $125,000 solution, far beyond their budget.

Even though he isn't a software engineer, Matt decided to take matters into his own hands. He attempted to build his own internal solution using low-code/no-code tools such as Microsoft Power Apps, but struggled to complete any projects. Matt also experimented with AI tools like ChatGPT, but still was unsuccessful building a full application to completion.
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After conducting further research, Matt discovered Pythagora. He quickly found it to be significantly more advanced than any other tool he had previously encountered, as its AI agents could learn from one another and produce functional code.  Using Pythagora, Matt achieved remarkable success, creating a fully functional application in just one day. Over the following 2-3 weeks, he added additional features to the app, which he now uses daily in his work.

With Pythagora, Matt not only found a viable solution but also transformed his initial struggles into a successful project that significantly benefited his team.

The app Matt built with Pythagora not only saved his team a substantial amount of money but also made their jobs much easier. The app enables seamless communication between their automated computers, allowing them to send XML work lists to each other and effectively process output files that specify where their automated robotics have moved samples between microplates.