AI Agent
Research Agent
Resolves "who or what is this actually about" from a single ambiguous query, then pulls the real content behind it, without a person searching multiple systems.
What is it?
An agent that resolves the specific player, team, or competition a query means, asking a clarifying question when a name is genuinely ambiguous rather than guessing, then streams the real articles, stats and picks behind that resolved entity.
What does it do?
- -Refuses to guess on ambiguous entities: asks one clarifying question instead of surfacing the wrong sport's data with false confidence.
- -Fast even cold: a two-tier cache serves the first request from a roughly 300-entity hot set in about 50 milliseconds, instead of a 9.8-second full-table fetch.
- -Self-healing: a fallback point-query catches long-tail entities that aren't in the cache yet, instead of returning a false no-match.
- -One resolved entity feeds three different content types, articles, stats, and picks, without re-resolving each time.
Why does it matter?
The name someone types is rarely the unique key a database needs. "England" alone could mean football, darts, or golf, guessing wrong and answering confidently is worse than asking one clarifying question.
Who is it for?
Engineering
How it works
A tiered-cache resolver backed by the platform's canonical entity tables, paired with two streaming tools: one for articles and stats, one for fixture-based picks. Both source facts exclusively from verified data, never model recall.