StratForge AI
AI-powered Counter-Strike 2 tactical intelligence platform
Project Overview
StratForge AI is a full-stack Next.js application that gives CS2 players access to professional-level tactical intelligence. The platform analyzes game state — map position, economy, round phase — and surfaces optimal strategies using deterministic logic and adaptive recommendation models. Features include CS2 radar maps with precise callout zone overlays (mapped to 1024×1024 radar coordinates), team profile management, round outcome workflow tracking, and a Vitest-driven test suite.
The Problem
CS2 players lack accessible, structured access to professional-level tactical knowledge. Strategies exist in videos and coaching sessions but not in a queryable, real-time format.
The Solution
Built a Next.js platform that encodes professional CS2 tactical logic as deterministic game-state analysis, making professional-level strategy recommendations available in real time based on map, economy, and round phase inputs.
Key Highlights
- Real-time CS2 strategy recommendations using deterministic game-state analysis
- Economy prediction and adaptive learning for round-phase-aware suggestions
- CS2 radar maps with precisely remapped callout zone overlays (1024×1024 coordinates)
- Team profile management with secure database-backed storage via Prisma
- Round outcome workflow tracking and history
- Vitest test suite covering round outcome workflows
- Deployed and live on Vercel at strat-forge-ai.vercel.app
- TypeScript throughout (99.4% of codebase)
Data / Request Flow
System Architecture
Code Spotlight
Real snippets from the codebase demonstrating key engineering decisions.
1interface RoundState {
2 teamMoney: number;
3 enemyMoney: number;
4 roundPhase: 'pistol' | 'eco' | 'buy' | 'force';
5 mapPosition: CalloutZone;
6 aliveCount: number;
7}
8
9// Deterministic strategy recommendation
10function recommendStrategy(state: RoundState): TacticalRecommendation {
11 const { teamMoney, enemyMoney, roundPhase, aliveCount } = state;
12
13 // Economy analysis
14 if (teamMoney < ECO_THRESHOLD)
15 return { buy: 'eco', rifles: 0, reason: 'Save for full buy' };
16
17 if (enemyMoney < RIFLE_THRESHOLD && roundPhase !== 'pistol')
18 return { buy: 'force', rifles: Math.min(aliveCount, Math.floor(teamMoney / RIFLE_COST)) };
19
20 // Map-position aware strategy
21 const calloutSuggestions = getCalloutStrategy(state.mapPosition, aliveCount);
22 return { buy: 'full', rifles: aliveCount, callouts: calloutSuggestions };
23}Challenges & How I Solved Them
Precisely remapping callout zones to actual 1024×1024 radar image pixel coordinates
Designing deterministic strategy logic that accounts for economy, map position, and round phase
Building a data model flexible enough to represent all CS2 map callouts and team compositions
Balancing real-time performance with complex tactical analysis logic
Results
- →Live production deployment on Vercel at strat-forge-ai.vercel.app
- →CS2 radar maps with accurate callout zone overlays for major maps
- →Secure team profile system backed by Prisma database migrations
- →Vitest test suite covering core round outcome workflows