Jellyfish's Telemetry Blind Spots
Span
The key split in this market is no longer dashboards versus no dashboards, it is telemetry versus true detection. Jellyfish can tell an executive how Copilot usage lines up with pull request speed because it already sits on top of GitHub, Jira, and finance data, but that approach is strongest when work happens inside systems GitHub exposes. Once developers switch to ChatGPT, Cursor, Claude Code, or other IDE and chat workflows, telemetry leaves blind spots that a content level detector is built to fill.
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Jellyfish is built for broad management visibility, not deep code attribution. Its core product ingests commits, pull requests, issue transitions, calendar events, and payroll data, then maps effort to projects and budgets. AI Impact fits that architecture as another management layer, alongside DevFinOps and board reporting.
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That makes Jellyfish look different from Span and LinearB in practice. Span sells a model that classifies code chunks as human or AI assisted across GitHub, GitLab, Jira, and IDE workflows. LinearB leans the other way, using repository and workflow data to power automation, PR bots, and GenAI impact modules tied to the existing toolchain.
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GitHub itself shows why telemetry based products emerged first. Its Copilot Metrics API exposed adoption and usage data for code completions and Copilot Chat in the IDE, which made it straightforward for platforms layered on GitHub to launch AI dashboards quickly. But that data only covers GitHub visible surfaces, not the full sprawl of external AI tools teams now use.
The market is moving toward systems that combine both views. Management platforms will keep owning budget, workflow, and executive reporting, but the next wedge is proving where AI actually wrote code across every surface developers use. The vendors that merge attribution, workflow context, and financial ROI into one system will define the AI governance layer for engineering.