AI / Sports Tech / Web Platform

AI TeamScout

An AI recruiting advisor built for the athlete — personalised school fit, NIL guidance, coach outreach, and a recruiting plan that updates as their stats do.

Live productAI evaluation engine

ReactNode.jsMongoDBOpenAI

A full-stack AI recruiting platform that pulls public recruiting profiles into one conversation and turns them into a ranked shortlist of schools.

The recruiting advisor answering a depth-chart question with a program-by-program breakdown and cited sources. — **The advisor, answering**Cited sources, and a next step the athlete can act on.

A tracked-schools board scoring each program out of 100 with a fit ceiling, category label and roster-movement tags. — **Fit, scored per school**Every tracked program scored, with the ceiling and what moved it.

A coach outreach dashboard showing send quota, open and reply rates, and a timeline of delivered, opened and replied events. — **Outreach, tracked**Sends, opens and replies — a workflow, not a chatbot.

The challenge

Recruiting information is scattered across recruiting sites, school pages, stats databases and social platforms. Athletes and families piece it together themselves, then make the decision on it.

What we built

A conversational recruiting advisor on top of a real product: athlete profiles, saved conversations, school tracking, coach-outreach drafting and subscriptions. Not a chatbot with a login screen.

How it works

Public recruiting profiles are parsed, normalised and injected into the model as context. Measurables mentioned in passing during a chat get detected, validated and proposed back as profile edits.

Client: React
Services: Node.jsOpenAIStripe
Platform: MongoDBAWS ECS/Fargate

The result

Research that used to span five tabs happens in one thread that keeps the athlete's stats and priorities between sessions.

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