Jellyfish Academy
Mastering Engineering Management through data-driven operational excellence.
The leading Engineering Management Platform that aligns technical execution with business strategy.
Jellyfish is the premier Engineering Management Platform (EMP) designed to provide visibility into engineering organizations' work, performance, and alignment. By 2026, Jellyfish has evolved beyond simple Git/Jira analytics into a comprehensive 'AI-for-Engineering-Operations' suite. Its technical architecture centers on the 'Jellyfish Allocation Engine,' which utilizes advanced Natural Language Processing and Machine Learning to automatically categorize engineering signals—such as commits, tickets, and PRs—into strategic buckets (e.g., Roadmap, Maintenance, Support). This allows CTOs to verify if their resources are focused on high-priority business goals. The platform integrates deeply with the modern SDLC stack, including VCS, project management tools, and HRIS systems, to provide a single pane of glass for organizational health. Its 2026 market position is defined by predictive capabilities, offering AI-driven forecasting for sprint completion and automated detection of developer burnout patterns. It is positioned as the 'ERP for Engineering,' moving the conversation from 'what is happening' to 'what will happen' and 'how do we optimize for impact.'
Uses NLP to automatically categorize Jira tickets and GitHub commits into strategic categories based on keyword extraction and metadata clustering.
Mastering Engineering Management through data-driven operational excellence.
Transform engineering velocity with AI-driven DORA metrics and predictive bottleneck detection.
The world's leading engineering intelligence platform for standards, component sourcing, and technical knowledge management.
Automate engineering velocity and business alignment with AI-driven workflow intelligence.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Monte Carlo simulations based on historical velocity to predict the impact of reallocating resources between projects.
A high-fidelity visualization of the flow of work from ideation to production deployment.
Qualitative developer experience surveys integrated with quantitative activity data.
Automated R&D tax credit reporting by mapping engineering hours to specific capitalizable projects.
Aggregated, anonymized data comparison against thousands of other engineering organizations.
AI-driven forecasting that alerts managers when a project's completion date is slipping based on current PR velocity.
CTOs often cannot prove that their teams are working on the highest ROI features.
Registry Updated:2/7/2026
Manual time-tracking for R&D tax credits is hated by engineers and inaccurate for finance.
High turnover due to invisible bottlenecks and excessive context switching.