Elevate engineering standards through validated technical assessments and AI-powered skill analysis.
Codility is a premier technical assessment platform engineered for enterprise-grade developer hiring and internal talent management. By 2026, the platform has fully integrated its 'IGGY' AI engine, which assists in automated task creation, plagiarism detection, and performance benchmarking. The technical architecture focuses on providing a secure, sandboxed environment that supports over 40 programming languages and various framework-specific stacks (e.g., React, Node.js, Spring). Codility’s market position is defined by its rigorous validation process for every coding task, ensuring that assessments are resistant to LLM-based cheating while maintaining a high correlation with real-world job performance. The platform serves as a critical bridge between talent acquisition and engineering leadership, offering deep-dive analytics into candidate problem-solving patterns, code efficiency, and edge-case handling. With the 2026 updates, Codility has expanded its 'CodeLive' suite to include real-time collaborative IDEs that simulate complex microservices environments, making it a robust solution for vetting senior-level architecture and system design capabilities.
Generative AI engine that crafts unique, validated coding problems to prevent 'canned' responses.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A low-latency, synchronized coding environment with built-in video and audio for remote interviews.
Algorithmic comparison of candidate code against millions of internal and public repositories.
Dockerized environments supporting full-stack framework tests (React, Angular, Django).
Anonymized candidate profiles and scorecards to focus purely on technical output.
Data visualization layer that benchmarks your candidates against global industry percentiles.
A mobile-responsive, accessible interface designed to reduce candidate anxiety and friction.
Manually screening thousands of entry-level resumes is inefficient and error-prone.
Registry Updated:2/7/2026
Standard algorithm tests fail to measure system design and multi-file architecture skills.
Coordinating external IDEs and video calls is disjointed and hard to track.