Jellyfish is rolling out features aimed at delivering real data on which AI tools actually work for software development, helping teams measure ROI and tool effectiveness.

Major users like DraftKings and Keller Williams are already leveraging Jellyfish to optimize AI spending, and the updates are available now.

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The Jellyfish AI Impact platform now offers end-to-end visibility of AI’s impact on productivity, quality, and value across the software development lifecycle.

As part of the same update, Jellyfish emphasizes a comprehensive view of AI’s impact across the SDLC, tying spend to outcomes.

CEO Andrew Lau stresses the goal of linking granular AI spend to delivery impact and providing clear visibility into tool value amid rising AI costs.

The Code Review Agent Dashboard enables measurement of AI code-review agents such as CodeRabbit, Graphite, and Greptile across the SDLC.

The industry context shows AI adoption in engineering is high, with about 90% of engineering teams using AI coding tools in 2025, yet many lack data to justify tool investments.

The updates reflect a broader trend of measuring AI’s practical impact across tooling and processes in the SDLC.

Multitool Comparison lets teams benchmark multiple AI tools side-by-side, comparing adoption, cost, and impact to identify the highest-value tools for specific use cases.

Jellyfish is addressing the common concern that organizations deploy AI in development without transparent evidence of its benefits.

Dynamic AI Tool Spend Dashboards provide real-time, usage-based spend tracking at team and project levels to tie spending to outcomes.

Jellyfish remains vendor-neutral, focusing on data to help engineering leaders determine which tools actually work for their teams.

The updates add support for Claude Code, Windsurf, and existing tools like GitHub Copilot, Cursor, Gemini, and Amazon Q, enabling multitool comparisons, the code review agent dashboard, and dynamic spend tracking.