To sire is to originate: to be the source a lineage traces back to. That's the whole premise: build the engine everything else runs on, and take responsibility for what runs on it.
Most prediction and trading tools are single-purpose: rebuilt from scratch for every new market, with no shared track record to point back to.
Own one ML engine outright, version it like infrastructure, and let every product (ours and our customers') trace its calls back to the same auditable core.
Three engineers leave data roles to build a shared ML engine for prediction and trading, instead of a one-off model for every new idea.
Gradient-boosted models plus a signal-confluence gate, built to be reused across markets instead of retrained from scratch each time.
The core engine gets pointed at its first market: real-world prediction events, priced and settled on-chain.
Three more products launch on the same engine: dual-exchange stock brokerage, an open task marketplace, and chat-native payments.
Ranked, not just listed: when two of these pull in different directions, this is the order that wins.
A signal we can explain beats one that merely backtests well. Every product ships with its accuracy history attached.
We don't bolt an API call onto someone else's model and call it a product. If it's core to what we ship, we built and trained it ourselves.
We'd rather ship fewer products and support each one for years than churn through versions.