Kaizen AI

Created by team Error 404 Not found on July 06, 2026
Unicorn Track

Kaizen AI is an intelligent multi-agent self-assessment platform inspired by the Japanese philosophy of Kaizen (改善), which emphasizes continuous improvement through small, consistent steps. The platform helps students, professionals, and job seekers evaluate their technical knowledge, problem-solving skills, and career readiness through adaptive AI-powered assessments that evolve with each user's performance. A Question Planning Agent structures the assessment by selecting appropriate topics, question formats, and difficulty levels. A Question Generation Agent, powered by Fireworks AI APIs , dynamically creates technical, conceptual, coding, and scenario-based questions in real time. During the assessment, an Adaptive Difficulty Agent continuously adjusts the complexity of questions according to the user's performance, ensuring an engaging and accurate evaluation. Responses are then analyzed by an Assessment Agent, which evaluates not only correctness but also reasoning, clarity, confidence, and overall understanding. Once the assessment is complete, a Skill Gap Analysis Agent identifies strengths and areas for improvement across different domains. A Recommendation Agent then generates a personalized learning roadmap with curated resources, practice questions, and actionable improvement plans tailored to the user's career goals. Finally, a Report Generation Agent presents detailed analytics through an interactive dashboard, including skill scores, topic-wise performance, progress tracking, and AI-generated feedback. Built using React/Next.js, FastAPI, LangGraph, PostgreSQL, Redis, and Docker, Kaizen AI leverages AMD GPUs for accelerated inference and efficient execution of open-source AI models, while Fireworks AI powers advanced reasoning, adaptive question generation, and intelligent evaluation. Security is ensured through secure authentication, encrypted data handling, prompt injection protection, rate limiting, and secure API management.

Category tags: