Scout Prospect Visit Analysis & Cost Optimization
An AI-first scouting-trip planner: plain-language requests become optimized itineraries — who to see, at which games, in what order — balancing coverage, travel, and reimbursement-grade cost.
High-fidelity flat vector product mockup in enterprise SaaS marketing style: a scouting-trip planner application window — a left panel listing an ordered itinerary as numbered stop cards with small calendar icons, a central stylized flat map card with circular venue dots connected by one royal blue (#004cce) optimized route line with subtle direction arrows and faint gray alternate routes, a bottom summary bar with three cost stat blocks. Soft pale gray (#f3f6fa) page background; white (#ffffff) panels with thin (#e3e9f1) hairline borders and very subtle drop shadows; charcoal (#16212e) primary marks; slate (#5d6b7b) secondary marks; blue used only for the route and one stop card. All text as greeked placeholder bars — no legible words, letters, or numbers. Crisp straight-on geometry, generous spacing, modern and corporate, no people, no photographs What it does
A scout tells the system, in plain language, what the next trip needs to accomplish — I need looks at these six prospects in the next two weeks, starting from home, back by Friday — and it returns an optimized itinerary: which prospects to see, at which specific games, in what order, with drive-versus-fly decisions made and a cost estimate a travel office would recognize.
The optimizer balances three objectives at once. Coverage: who still needs looks, weighted by priority and by how perishable the opportunity is — schedules end, players get shut down. Travel: efficient driving circuits where geography allows, flights where it doesn’t, sequenced so the miles serve the looks rather than the other way around. Cost: a reimbursement-grade model — mileage, per diem, lodging, rental, airfare — so every itinerary carries a defensible budget line, not a guess.
Then it makes each stop worth more. For every player on the itinerary, the system produces a short, bulleted scout prep brief drawn from the club’s real scouting material — evaluations, ball-tracking data, prior notes, makeup and background context — so the scout arrives already current. Trips export cleanly for the people who approve them. One deliberate design choice from the demo build: no in-app map. Scouts don’t need a prettier map; they need the right list in the right order.
Who it’s for
Scouting directors and coordinators who own coverage plans and travel budgets; area scouts and crosscheckers who live the itinerary; and the operations staff who have to reconcile what a trip cost against what it produced.
The quantitative methods
Prize-collecting route optimization — the operations-research formulation for “you can’t see everyone, so which subset, in which order, pays the most?” — over the real game schedule; drive-circuit versus flight segmentation; federal-style reimbursement cost modeling; coverage scoring with priority decay; and an agentic tool-use loop that lets all of it be driven conversationally, with the optimizer producing every number.
Example outcomes
Representative outcomes: more qualified looks per travel dollar across a season; coverage gaps surfaced while there’s still schedule left to fix them; travel budgets that can be defended line by line; and scouts who spend their evenings watching players instead of fighting spreadsheets and mapping tabs.
From sample to engagement
An engagement version plugs the optimizer into your world: your prospect priorities, your scouts’ home bases and territories, your reimbursement policy, your prep material. The PoC typically re-plans a set of trips your department already took — so you can see, in dollars and looks, what the optimizer would have done differently.