PRE-SEED · SAFE · USD $300,000

The AI Scout that watches every match.

Soccerlytica turns any football video into a professional scouting report — automatically. We are building the operating system for football scouting, starting where the talent is densest and the data is thinnest.

See the ask → Talk to the founder
Mahmoud Jajah · Founder & CEO
Accra · Ghana
soccerlytica.com
Vision & Mission

Discovery by intelligence, not connections

Mission
Democratise football scouting using artificial intelligence.

Every year, countless gifted players go unseen because scouting depends on who you know and who happens to be watching. We replace that lottery with intelligence that is available to every club, academy and player on Earth.

The vision
  • The world's most intelligent AI Scoutwatching every match, everywhere
  • The operating system for scoutingthe layer clubs and academies run on
  • Discovery for every talented playerfound by AI, not by connections
  • The largest football intelligence platformproprietary performance data at scale
The Problem

Scouting is broken — and millions stay unseen

Traditional scouting cannot cover the game. It is expensive, slow, subjective and manual — so most clubs run tiny scouting departments, and whole regions go uncovered.

Expensive

Elite scouting costs more than most clubs can spend

Slow

Days of manual video work per player

Subjective

Opinion and bias over measured performance

Limited

No human can watch every match

The cost of the status quo
Millions
of talented players never scouted
~0
structured data on grassroots footballthe game's largest blind spot
Africa
produces elite talent, receives little exposure
The Solution

An AI Scout that never stops watching

Watches every match

Ingests any football video — grassroots to pro

Reports in minutes

Elite-grade scouting output, not days later

Never tires, never misses

24/7 coverage no human department can match

A fraction of the cost

Scouting economics reset by automation

Scales infinitely

One agent, unlimited matches and players

Gets smarter over time

Every match improves the models

⚡
Upload a match. Get a professional scouting report. That is the entire product experience — and it changes who gets discovered.
The Core Product

Soccerlytica Scout AI Agent

We are no longer an analytics company. Everything revolves around one flagship product: an autonomous AI agent that watches football like an elite scout — and writes the report.

  • It acts like a professional scoutwatching every minute of every match
  • It produces elite-grade reportsidentical in form to a top human scout's
  • It works autonomously, end-to-endvideo in, decision-ready intelligence out
Computer Vision LLMs Vision-Language Models AI Agent
Scout AI Agent — match_04.mp4
AI Analysing 22 players · 90:00 · generating report…
94.2
Top rating
12
Standouts
48s
Report time
What It Does

From raw video to decision-ready intelligence

Perceive

  • Detect every player
  • Track movement
  • Recognise events
  • Measure performance

Reason

  • Identify strengths & weaknesses
  • Tactical analysis
  • Compare, rank & recommend
  • Predict player potential

Report

  • Player, recruitment & transfer reports
  • Academy, opposition & coach reports
  • Auto-generated PDFs
  • Player history & development timelines
Upload video→ Detect & track→ Analyse & rate→ Scouting report PDF
AI Capabilities

The intelligence stack behind the agent

Vision

YOLO detection · player & ball tracking · pose estimation · action recognition · event detection

Language & Reasoning

Large Language Models · Vision-Language Models · speech recognition · tactical & performance intelligence

Agent

Autonomous AI-agent workflow · end-to-end report generation with no human in the loop

The pipeline
1
Ingest & decode video
FFmpeg · OpenCV
2
Detect & track
YOLOv8 · ByteTrack · pose
3
Recognise events
action recognition models
4
Model performance
ratings · xG · tactical metrics
5
Generate report
LLM + VLM autonomous agent
Product

Elite-grade output, instantly

Player heat map
Passing network
Shot map · xG
Player radar
Pace Shoot Pass Dribble Physical Defend
Ranking dashboard
K. Mensah94
A. Diallo91
J. Owusu88
S. Traoré85
Transfer recommendation
STRONG BUY
Kofi Mensah · RW · 18
Similarity to profile: 92% · projected value uplift · fits system
Why Now

Three curves just crossed

🧠

AI crossed the threshold

Computer vision, LLMs and agentic workflows are now good enough to analyse real match video autonomously.

💽

Compute got cheap

Cheaper GPUs and cloud infrastructure collapsed the cost of processing video into data.

⚽

Football went digital

Clubs record matches and demand data-driven recruitment; sports tech is compounding fast.

19.8%
Computer-vision market CAGR
to 2030 — Grand View Research
~21.6%
AI-in-sports market CAGR
Grand View Research
~46%+
AI-agents market CAGR
GVR / MarketsandMarkets
Market Opportunity

Riding four compounding markets

TAM$8.4B
SAM$1.9B
SOM$60M
AI in sports (2025)$10.6B
Sports analytics (2025)$5.7B
Sports technology (2024)$18.9B
Computer vision (2024)$19.8B
TAM — football's share of sports analytics, AI-in-sports & scouting software. SAM — football data, scouting & recruitment software, globally. SOM — near-term: African & emerging-market clubs, academies, agents. Plus global buyers of African talent — demand is worldwide.
Sources: Grand View Research (sports analytics $5.7B 2025→$23.1B 2033, 18.5% CAGR; AI-in-sports ≈$10.6B 2025, ≈21.6% CAGR; computer vision $19.8B 2024→$58.3B 2030; sports technology $18.9B 2024→$61.7B 2030); Deloitte Annual Review of Football Finance 2025. TAM/SAM/SOM are illustrative estimates.
Customers

Everyone who needs to know who is good

Buy & Deploy

  • Professional clubs
  • Academies
  • Football associations
  • National teams

Discover & Trade

  • Agents
  • Scouts
  • Recruiters
  • Media

Learn & Grow

  • Universities
  • Sports scientists
  • Players
  • Parents
◎
We enter through academies and grassroots clubs — where adoption is fastest and data is richest — then expand to professional clubs, federations, and the global market that buys talent.
Business Model

SaaS today, data & marketplace tomorrow

Recurring SaaS — core

  • Tiered subscriptionsScout, Academy, Club & Federation plans — monthly / annual
  • Enterprise licensingclubs, leagues and associations
  • API & AI creditsusage-based access to the agent

Expansion revenue

  • Data licensingproprietary performance data
  • Recruitment marketplacetransaction fees on talent
  • Premium reports & Enterprise AIhigh-value, custom output

Scout

$
individual

Academy

$$
teams

Club

$$$
pro

Federation

$$$$
enterprise
Competitive Landscape

AI-native and built for the whole game

AI-first Legacy /
Costly Accessible AI-native / autonomous → Affordable & accessible → Soccerlytica StatsBomb SciSports SkillCorner Wyscout Catapult Hudl Veo InStat

Incumbents are powerful but built for the elite, top-down game: expensive, human-in-the-loop, and dependent on data that already exists. None is an autonomous AI agent that manufactures scouting from raw video.

  • Hudl · Wyscout · InStatvideo & databases — manual, professional-priced
  • StatsBomb · SciSports · SkillCornerelite data & models — not grassroots-native
  • Veo · PlayerMaker · Catapultcapture & wearables — not autonomous scouting
Why Soccerlytica Wins

Ten reasons the advantage compounds

AI-first

autonomous agents, not tools

Built for Africa

grassroots-native from day one

Affordable

priced for real clubs

Fast

reports in minutes

Autonomous

no human in the loop

Modern architecture

cloud-native, scalable

Proprietary data

a dataset no one else has

Scalable

one agent, infinite matches

Continuous learning

better with every match

Network effects

more data → better AI → more users

◈
The moat is proprietary data plus continuous learning: every match analysed makes the AI better and the dataset deeper — an advantage a later entrant cannot simply buy.
Technology

A cloud-native AI architecture

Client
Next.js web app · dashboards · upload
API
FastAPI · Python · Celery · Redis
AI Core
YOLOv8 · ByteTrack · OpenCV · FFmpeg · LLMs · VLMs
Data
Supabase · PostgreSQL · object & vector storage
Infra
cloud GPU compute · MLOps · monitoring · security
Video → Intelligence
1
Video ingest
FFmpeg / OpenCV
2
Detect & track
YOLOv8 / ByteTrack
3
Events & metrics engine
4
LLM + VLM agent
5
Report & data store
Go-to-Market

Win Ghana, then compound outward

Stage 1
Ghana
Stage 2
West Africa
Stage 3
Africa
Stage 4
Europe
Stage 5
Global

Acquisition

  • Football academies & grassroots clubs
  • Professional clubs & federations
  • Strategic partnerships

Demand engine

  • Content marketing & AI demonstrations
  • Football events & scouting conferences
  • Discovery stories — earned media
Roadmap

One agent becomes a platform

Phase 1

Scout AI Agent

MVP — video to scouting report

Phase 2

AI Tactical Analyst

team & opponent tactics

Phase 3

AI Recruitment Assistant

shortlists & transfer intel

Phase 4

AI Opposition Analyst

pre-match intelligence

Phase 5

AI Sporting Director

squad & strategy decisions

Phase 6

Football Intelligence Platform

the operating system

Year 1 · Pilot

MVP · academies · players · videos analysed · first models

Year 2 · Launch

commercial launch · revenue · partnerships

Year 3 · Scale

expansion · AI marketplace · enterprise customers

Financial Projections

Illustrative five-year trajectory

$60KY1
$520KY2
$1900KY3
$4800KY4
$9600KY5
Illustrative ARR (USD 000s) — scenario, not a forecast.
~80%
Gross margin at scale
SaaS + data economics
3–4×
LTV / CAC target
efficient, academy-led GTM
~Y5
EBITDA breakeven
base scenario
$9.6M
Illustrative Y5 ARR
base case
USD 000sY1Y2Y3Y4Y5
Revenue05201,9004,8009,600
Gross profit03601,4103,7007,700
EBITDA(320)(540)(430)2602,400
Customers—2201,4005,20013,000
Illustrative model from stated assumptions (Appendix). Not a forecast; actual results will differ.
Use of Funds

USD $300,000 · ~18 months of runway

AI research & product engineering40%
Computer vision & model training20%
Customer acquisition & sales15%
Cloud infrastructure & GPU compute10%
Operations, legal & compliance10%
Working capital & contingency5%
18-month runway
$300KM0
$205KM6
$110KM12
$18KM18

Capital concentrated on product and data — ~60% to AI engineering, CV and model training — to reach the pre-seed milestones that unlock a Seed round.

Founder

Built by an operator and ecosystem-builder

Mahmoud Jajah — Founder & CEO of Soccerlytica
Mahmoud Jajah
Founder & CEO
Accra, Ghana · mj@soccerlytica.com

Ghana Embassy, Saudi Arabia

international relations & stakeholder engagement

ICRC (Red Cross), Nigeria

complex, large-scale programme execution

Founder, ZongoVation Hub

building an African innovation ecosystem

AI entrepreneur & developer

builds the product, not just the vision

Why Mahmoud: an operator who executes hard programmes across many stakeholders, is native to the African football and startup context, and is the architect of Soccerlytica's data-first strategy — a builder for a data-moat company.
Investment Opportunity

Raising $300K to reach Seed-ready

$300,000
Pre-seed round
SAFE
18 months
Runway
milestone-driven
Seed
Next round
de-risked by traction
Milestones before Seed
✓Production AI Scout Agentshipped & in use
✓50+ academiesdesign partners live
✓100+ clubspaying or piloting
✓20,000 player profilesproprietary dataset
✓100,000 match videosanalysed at scale
✓First enterprise customers+ recurring SaaS revenue

Every football match contains undiscovered talent. We make sure none goes unseen.

Founder Mahmoud Jajah · Founder & CEO
Email mj@soccerlytica.com
Base Accra, Ghana
Web www.soccerlytica.com