Joining AMID AI Labs Fellowship
Machina Sports is now part of AMID AI Labs' Fellowship cohort. This is a research-focused program designed to help AI startups working on real industry problems. Access to AMID's community means direct connection with researchers, practitioners, and other companies testing production AI systems.
What the Fellowship Actually Provides
AMID runs a closed network of AI founders and researchers solving problems that look like ours: deploying models at scale, managing data pipelines, handling model drift in production, and shipping features that actually move business metrics.
The fellowship gives us:
- Access to AMID's research papers and frameworks on production AI systems (especially relevant for our agent architecture)
- Direct mentorship from founders who've shipped AI products in media, entertainment, and sports
- Peer learning with other companies working on similar infrastructure problems
Why This Matters for Machina
We're building a serverless stack for sports organizations. That stack needs to handle:
- Real-time inference (game recaps published 30 seconds after final whistle)
- Multi-model orchestration (our agents coordinate across content generation, sentiment analysis, and data retrieval)
- Cost optimization (sports organizations care about margins; we need to reduce token costs without sacrificing quality)
AMID has solved these problems. Their founders have shipped similar systems at scale. Getting direct access to their thinking accelerates how fast we can iterate on our infrastructure.
What We're Focusing On
Our work with AMID will concentrate on three areas:
- Agent architecture optimization: How to coordinate multiple AI models without creating brittle dependencies
- Cost-efficient inference: Reducing token spend for content generation without losing quality
- Evaluation frameworks: How to measure when our agents are performing well vs. when they're hallucinating or degrading
These aren't new problems, but they're the ones that determine whether AI products actually work in production.
Next Steps
We're publishing our learnings from AMID as we go. If you're building on our SDK or considering using Machina for content automation, these improvements will ship directly into the product.
Related: Building a Semantic Layer for Sports for how agent coordination drives personalization at scale.
Related: RAG in Sports: Why DIY Fails shows production challenges AMID expertise solves.
Related: Personalized Fan Engagement demonstrates infrastructure-level improvements from better research.