Maple is being built with a deliberate emphasis on determinism and transparency in how results are computed — "the engine is the product" is meant literally: a shared foundation designed to turn fragmented data and models into reproducible research evidence.
Deterministic by design
The calculations at the core of Maple — testing, scoring, validation — are designed around a deterministic engine: the same hypothesis and the same data should always produce the same result. That matters because it means the numbers Maple is designed to produce are meant to be reproducible and auditable, not the output of an opaque model that could answer differently each time it's asked.
Where AI fits, and where it doesn't
AI is designed to help express a hypothesis — turning a plain-English idea into structured logic, and helping explain a result in plain language. It is not designed to decide what the underlying calculations are. That separation is deliberate: Maple's target is the research-infrastructure burden, not AI-generated investment opinions.
Market data
Maple is designed to work across categories of financial data — market, fundamental, macroeconomic, and alternative — sourced from established providers, with requests that require an API key routed through a server-side layer rather than called directly from a browser, so provider credentials are never exposed client-side.
No trading connectivity
Maple is not designed to connect to a brokerage or exchange account, place a real order, or move real funds under any circumstance. That's a deliberate scope boundary, not a missing feature: Maple's target is the research and evidence layer, not execution.