Jellyfish
The leading Engineering Management Platform that aligns technical execution with business strategy.
Transform engineering velocity with AI-driven DORA metrics and predictive bottleneck detection.
CodeAI Analytics is a next-generation engineering intelligence platform designed to bridge the gap between raw Git data and executive-level business outcomes. By 2026, the platform has evolved from simple commit tracking to a sophisticated AI-orchestrated engine that predicts project delays before they occur. It leverages Large Language Models (LLMs) to analyze code churn, PR sentiment, and knowledge silos across distributed teams. The technical architecture utilizes a non-invasive integration layer that connects directly to GitHub, GitLab, and Bitbucket, processing metadata through a proprietary risk-scoring algorithm. Unlike traditional analytics tools, CodeAI Analytics provides 'Remediation Paths,' suggesting specific workload rebalancing or architectural reviews to maintain a high velocity. Its 2026 market position is defined by its 'Context-Aware Productivity' feature, which differentiates between high-complexity engineering breakthroughs and low-value technical debt accumulation. It is compliant with modern data sovereignty laws, offering localized data processing for global enterprises.
Uses a Random Forest regressor combined with LLM analysis of PR comments to identify blocked workflows before they impact milestones.
The leading Engineering Management Platform that aligns technical execution with business strategy.
Automate engineering velocity and business alignment with AI-driven workflow intelligence.
The world's leading engineering intelligence platform for standards, component sourcing, and technical knowledge management.
Mastering Engineering Management through data-driven operational excellence.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Graph-based analysis of file ownership and code review patterns to visualize areas where only one developer has expertise.
NLP processing of developer feedback and review comments to measure team morale and friction points.
Quantifies technical debt by correlating code complexity metrics with bug frequency and churn rates.
Categorizes work into New Features, Maintenance, and Infrastructure using AI classification of commit messages.
Granular measurement of time spent in Coding, Pickup, Review, and Deploy phases.
Compares team performance against industry peers based on stack, team size, and industry vertical.
The engineering team is consistently missing deadlines but cannot identify the specific bottleneck.
Registry Updated:2/7/2026
Monitor the 'Velocity Trend' dashboard for improvement over 2 sprints.
High turnover in a core backend team due to invisible workload pressure.
Product owners and engineers disagree on whether to build new features or refactor code.