Build for decisions that happen live.

Unlike most software domains, live sports schedules are dynamic in unpredictable ways - weather delays, postponements, format changes mid-season, late additions. Most scheduling systems assume a fixed calendar. Sports doesn’t have that. We built a custom scheduler for AltSportsData that handles these edge cases without requiring a human to manually trigger runs when something changes.

With the right architecture, yes - but not by asking GPT to “calculate odds.” The problem with LLMs and math is they’re probabilistic reasoners, not calculators. Our approach: decompose the odds generation into discrete steps, each with explicit math validation. The LLM handles context, reasoning, and narrative. The deterministic layer handles the numbers. Langfuse gives you full traceability at every step so you can audit any output.

It means each data transformation is a self-contained, reusable component. When AltSportsData wants to add Formula Drift to their platform, they’re not writing new ETL code - they’re assembling existing components in the visual pipeline builder. Adding a new sport went from a multi-week engineering project to an hours-long configuration task. That’s what composable means in practice.

Yes. Both AltSportsData and our betting platform client have Autonomous engineers embedded on their teams. These are engineers who own pipelines, commit code daily, and are accountable for outcomes - not consultants who deliver a slide deck and disappear. We can work alongside your existing team or operate as your dedicated data engineering function.

Through AltSportsData, we’ve worked with F1, Cricket Australia, Supercross, Super Motocross, World of Outlaws, Formula Drift, and Combat Sports. On the betting side, we’ve built for College Football and NFL. The composable architecture means adding new sports is fast - the data engineering patterns transfer. We’ve also worked with racing formats, which have some of the most complex scheduling edge cases in sports data.