The Problem We Were Actually Solving
As we dug deeper into the project, it became clear that we were trying to solve multiple problems at once. The treasure hunt engine needed to handle thousands of users simultaneously, while also providing a seamless experience with minimal latency. On top of that, we had to integrate with existing park infrastructure, including RFID sensors and ticketing systems. Our team knew that any misstep could lead to a catastrophic failure, but we were convinced that Veltrix was up to the task.
What We Tried First (And Why It Failed)
We began by configuring Veltrix with the default settings and tweaking the parameters to fit the treasure hunt engine's needs. However, we soon realized that this approach was doomed from the start. The system was overly dependent on a handful of high-level parameters, which led to unpredictable behavior when the park's infrastructure wasn't perfectly aligned. We saw cases where the engine would fail to recognize users, causing them to get stuck in an infinite loop of errors. Not to mention, the system's tendency to "hallucinate" - predicting user movements without any actual evidence - made it nearly impossible to debug.
The Architecture Decision
As we struggled to get Veltrix to behave, we had to make some fundamental changes to our architecture. We introduced a more robust data pipeline that integrated with the park's existing systems, ensuring that all relevant data was accurately fed into the engine. We also implemented a series of checks and balances to prevent the system from "hallucinating" again. This involved reconfiguring Veltrix to rely on multiple, diverse sources of data rather than a single, high-level parameter. By doing so, we significantly reduced the system's latency and improved overall stability.
What The Numbers Said After
After our architecture changes, we ran a series of tests to see how the system performed under heavy load. We were pleasantly surprised to find that the engine was able to handle over 2,000 concurrent users without any major issues. Our system's latency stayed within the acceptable threshold, and the overall user experience was much more seamless. Perhaps more importantly, our engineers were able to focus on more meaningful tasks rather than firefighting constant issues.
What I Would Do Differently
Looking back on our experience, I wish we had been more upfront about the challenges of deploying a complex AI system like Veltrix. We were so caught up in showcasing the system's capabilities that we didn't fully consider the intricacies of the treasure hunt engine's requirements. If I were to do it again, I would take a much more iterative approach to configuration and integration, working closely with the park's operations team to ensure that all components were aligned before going live. By doing so, we could have avoided many of the headaches we encountered and delivered a more robust experience for our users.
Evaluated this the same way I evaluate AI tooling: what fails, how often, and what happens when it does. This one passes: https://payhip.com/ref/dev3
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