

Pitcrew AI is an innovative technology company based in Australia. As their product advanced, they needed a new QA team that could support continuous training and improving AI algorithms.
Their AI generated more high-quality scans than their team could keep up with. The AI flagged possible issues, but someone still had to review each scan, confirm if the alert was real, and spot tiny anomalies that the system might miss. They needed a QA team dedicated to improving accuracy and preventing false positives before anything reached their clients.
However, they faced some challenges:
Pitcrew AI wasn’t looking for a general team. They needed people who could learn fast, work with precision by training their AI systems, and keep operations running around the clock. Check how Athyna supported them in this key moment.

Athyna partnered with Pitcrew AI to understand the skill set required. Using our global talent platform, we sourced and endorsed eight qualified QA specialists. Each candidate was screened for accuracy, consistency, and the ability to make judgment calls on highly technical imaging data.
The partnership delivered strong results from day one. Five talents were hired out of the eight we endorsed, showing a clear match between Pitcrew’s needs and the talent we sourced. Even better, the very first hire they made was with the first candidate we presented.
To support Pitcrew’s continuous workflow, we assembled a blended global QA team:
This group reviews truck scans, flags irregularities, verifies system alerts, and provides high-quality data that trains Pitcrew’s AI to become more accurate. Their work supports Pitcrew’s shift toward quality-first review and creates a reliable foundation for scale.
Pitcrew AI had a strong training structure, and we helped match them with talent who could adopt it quickly. Once hired, the team ramped up fast and began contributing to real QA cycles within weeks. And here are the outcomes:
By building this team through Athyna, Pitcrew AI saves over 93,000 USD each year, creating room to reinvest in product development, training, and operational efficiency. The result is a cost-effective QA function that improves both day-to-day performance and long-term AI accuracy.
Here are some other performance wins:

