Case Studies › Winthrop Intelligence
Sports Technology & AI Analytics

Turning scattered athletic data into a recruiting edge

Techdots designed and built Winthrop Intelligence's core platform — a multi-tenant SaaS tool that aggregates athlete performance data, applies AI-driven fit scoring, and surfaces actionable recruiting recommendations to athletic directors and coaching staff. We took the product from whiteboard to paying customers in under six months, building the data ingestion layer, scoring engine, and the dashboard interface athletic programs use every day.

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Winthrop Intelligence
Sports Technology & AI Analytics
74%
Reduction in time-to-shortlist for recruiting staff
3.2x
More prospect profiles evaluated per recruiting cycle
40+
D1 and D2 athletic programs onboarded at launch
20 weeks
timeline
This engagement is best for
SaaS founders who have domain expertise but need a technical partner to build v1
Analytics platforms that need AI scoring layers on top of complex multi-source data
Sports technology startups targeting institutional buyers (universities, leagues, associations)
Products where the data pipeline is as important as the UI
The Transformation

Before & After

Before
✕ Recruiting coordinators maintained separate spreadsheets across multiple coaches, with no shared view of the prospect pipeline
✕ Performance data from different sports lived in siloed vendor exports — there was no unified athlete profile
✕ Athletic directors had no way to compare recruits against current roster benchmarks without pulling manual reports
✕ Roster management decisions were made based on intuition and anecdote, not data-backed position gap analysis
✕ The founding team had a clear vision and domain knowledge but no engineering capacity to build a production system
After
✓ A unified prospect database with AI-generated fit scores based on position needs, academic standing, and athletic performance metrics
✓ Automated ingestion of data from NCSA, Hudl, and college sports stats APIs into a normalized athlete profile model
✓ Athletic directors can view position-level roster depth, projected attrition, and recruiting gap analysis on a single dashboard
✓ Coaching staff share live recruiting boards with notes, status tracking, and priority scoring — replacing email chains and spreadsheets
✓ The platform launched on schedule and onboarded 40+ programs in the first quarter, validating product-market fit
What We Built

Deliverables & Scope

Every item below was chosen because it directly addressed a business bottleneck — not because it was technically interesting.

01
Multi-tenant Rails API with role-based access for athletic directors, head coaches, position coaches, and recruiting coordinators
02
AI fit-scoring engine that ranks prospects against a program's current roster profile using position, GPA, eligibility year, and performance percentiles
03
Data ingestion pipeline connecting NCSA Athletic Recruiting, Hudl video analytics, and NCAA eligibility data feeds into a normalized PostgreSQL schema
04
Roster depth and attrition forecasting dashboard built in React with dynamic position-group breakdowns and multi-year projections
05
Recruiting pipeline board with kanban-style stage tracking, shared notes, contact logging, and offer status management
06
Admin and white-labeling layer allowing Winthrop Intelligence to provision new athletic programs with sport-specific templates
ROI Logic

Why This Generated
Real Business Value

Athletic directors and head coaches spend an estimated 15–20 hours per week on recruiting research that is largely duplicated across staff. By centralizing prospect data and surfacing AI-ranked shortlists, Winthrop Intelligence recaptured that time and directed it toward relationship-building — the part of recruiting that actually converts. Programs that spend recruiting budget more precisely also reduce the cost of missed offers and roster mismatches, which can cost hundreds of thousands of dollars in scholarship reallocations.

Key Outcomes
74%
Reduction in time-to-shortlist for recruiting staff
3.2x
More prospect profiles evaluated per recruiting cycle
40+
D1 and D2 athletic programs onboarded at launch
Why It Worked

The Decisions That
Made the Difference

Good execution matters. But the right early decisions matter more.

01
We prioritized the data model before any UI — getting athlete normalization right across sports, positions, and data sources made every downstream feature faster to build and more accurate
02
The AI scoring engine was designed to be explainable, not just a black box — coaches can see exactly why a prospect ranked highly, which drove adoption among skeptical staff
03
We built multi-tenancy and sport-specific configuration into the schema from day one, so adding a new sport or program required configuration rather than engineering
04
Close collaboration with Winthrop Intelligence's domain experts meant every feature mapped to a real workflow athletic departments already used — there was no wasted surface area
Tech Stack
Ruby on Rails React.js PostgreSQL Python AWS
Integrations
NCSA Athletic Recruiting API Hudl video analytics NCAA Eligibility Center data feeds Sportradar college stats API SendGrid (offer and communication tracking)
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