Architecture
Before & After
Before
Fragmented workforce data across multiple systems, no AI-driven insights for pattern detection, manual compliance reporting, and slow query performance on large datasets.
After
Unified workforce analytics platform with AI-powered anomaly detection, real-time compliance dashboards, fast Redis-cached queries, and automated report generation. NextAuth provides secure role-based access.
Results
Technical Challenge
Real-time Anomaly Detection at Scale
The Problem
Workforce data streams contain subtle patterns that humans miss—overtime creep, unusual scheduling patterns, compliance drift. Manual review was impossible at scale; automated rule-based systems generated too many false positives.
The Solution
Leveraged Mistral AI for pattern analysis against historical workforce baselines. Rather than hard-coded rules, the system learns normal patterns per worker/role/department and flags deviations with confidence scores. Human-in-the-loop feedback improves accuracy over time.
Redis caching of frequent queries (dashboard loads, report generations) reduced database load by 70%, keeping the AI-powered insights snappy even with 50K+ daily data points.
Tech Decisions
Next.js 16 with Turbopack
The dashboard required instant page loads and fast hot module replacement during development. Turbopack's Rust-based bundler provided 10x faster builds compared to webpack. Server Components reduced client-side JavaScript, improving initial load performance.
Prisma for Type-Safe Data Access
TypeScript end-to-end meant database queries should also be typed. Prisma's generated types propagate from the schema through the API layer to the frontend, eliminating an entire class of runtime errors related to data shape mismatches.
Mistral AI for Pattern Recognition
Traditional rule engines struggle with context. Mistral's reasoning capabilities understand natural language queries about workforce data ("show me departments with unusual overtime patterns this quarter") and can explain its anomaly scores in plain English.