How Plai Labs shipped a generative AI social app in 4 months

The challenge

Plai Labs needed production machine learning capability before they could prove the product. That meant researchers who understood image-generation models, engineers who could run them at consumer scale, and product thinking to connect both to a social app, all assembled fast enough to matter, at the exact stage when hiring senior ML talent directly is slowest and hardest.

The solution

Eventum built and led the ML effort end to end:

• A 6-person team, placed pre-seed. Eventum recruited and embedded six machine learning researchers, engineers, and product managers, and provided the advisory and consulting layer to direct them while Plai Labs’ own organization was still forming.
• A Stable Diffusion fine-tuning and inference system. The team built the pipeline for creating fine-tuned image-generation models and serving them: training infrastructure for personalization, and an inference system engineered for consumer traffic rather than demos.
• Deployment at consumer scale. The fine-tuned models went to production serving tens of thousands of users, powering AI avatars and personalized generation inside the product.

The result

PlaiDay launched 4 months after the engagement began, with generative AI as the core of the product rather than a feature bolted on. And the clearest signal of the team’s quality: after the engagement, Plai Labs hired 4 of the 6 specialists Eventum had placed as full-time employees.

Why it mattered

Pre-seed companies rarely get access to senior ML talent at all, let alone a coordinated team with its own technical leadership. Placing the team before the funding round meant the $33 million arrived with the technical foundation already proven, not still hypothetical.

Conclusion

Eventum gave Plai Labs a working ML organization before they could have built one themselves: six specialists, a production Stable Diffusion system, a launched product in four months, and four permanent hires at the end of it.