Background
High-Fidelity MVP

AI-Powered
Gameplay
Analysis System

Transforming screen recordings into actionable performance insights with high-fidelity computer vision and neural networks.

Apple
Android
Web
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AI Gameplay Analysis

Project Overview

Competitive esports requires rigorous review of gameplay. However, existing methods are either painfully manual or blocked by restricted game APIs. This system bypasses these limitations by "watching" the screen just as a human coach would, but with the speed and precision of AI.

PROBLEM

Manual review time is slow; Restricted game APIs.

AUDIENCE

Competitive Gamers & Esports Coaches.

VISION

Automated, deep performance insights from video.

IMPACT

60-70% reduction in session review time.

Automate Analysis

Automate Analysis

Removing the bottleneck of manual clip tagging.

Scalable Systems

Scalable Systems

Works across titles without requiring API access.

Deep Insights

Deep Insights

Extracting positioning, timing, and aim metrics.

Real-time Core

Real-time Core

Low-latency processing for immediate feedback.

The Tech Stack

A robust combination of computer vision libraries and modern web architecture.

Python

Python

YoloV8

YoloV8

Scikit_Learn

Scikit_Learn

Tesseract_OCR

Tesseract_OCR

OpenCV

OpenCV

Next.js

Next.js

Node.js

Node.js

MongoDB

MongoDB

THE CHALLENGE

High Variability & Real-Time Constraints

Handling diverse resolutions, bitrates, and visual skins across different gaming setups presented a significant hurdle. The system needed to process 60 frames per second without missing critical events like a millisecond-long killshot update.

Skin & Cosmetic Variability

Skin & Cosmetic Variability: AI models must ignore custom player skins.

In-game dynamic lighting

In-game dynamic lighting: Changes in game maps affecting OCR reliability.

Processing Overhead

Processing Overhead: Balancing depth of analysis with processing cost.

Processing Frame 4529...
// Processing Frame 4529...
DETECTED: Enemy_Player [Conf: 0.90]
DETECTED: Head Hitbox [Conf: 0.89]
OCR_READING: "killed by SnipeHero_01"
Analysis buffer: 92% complete
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01. Optimized Video Pipeline

Our pipeline handles raw recordings by intelligently down-sampling and focusing only on 'action-heavy' sequences, reducing redundant computation by 40%.

Optimized Video Pipeline
Computer Vision Event Detection
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02. Computer Vision Event Detection

Using custom-trained YOLOv8 weights, we detect unique character abilities, tactical equipment usage, and positioning errors with coach-like precision.

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03. The Insight Engine

Beyond raw data, our engine correlates positioning with win rates, providing heatmaps and specific coaching advice like 'You are over-peeking corners in 2v1 situations.'

The Insight Engine
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Client Overview

Our client is a UAE-based esports organization specializing in competitive gaming and event management

Future Vision

The Future of Competitive Play

From individual growth to team-wide strategic dominance, our AI-driven approach is setting the new standard for performance analysis in the digital age.

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