task2vec turns years of Jira tickets into a semantic map of expertise — showing how individuals evolve, where the project is heading, and whether the two are moving together.
Open Spring explorer AI Readiness analysis AI Work Cockpit Score a ticket →Not all engineering work is equal. Some tickets are repetitive patterns the team has solved dozens of times. Some sit at the edge of what anyone knows. Most are somewhere in between. The problem is that nobody has ever had a reliable way to tell them apart — until now.
task2vec embeds every ticket into a semantic map built from your project's full history. That map makes three things visible at once: which work is safe to hand to an AI agent, which needs an AI draft with a human checking the result, and which problems are genuinely hard — the ones that require deep expertise, careful judgment, and the kind of engagement that actually grows an engineer's skills.
The goal is not to replace engineers. It is the opposite: free them from repetitive work so their time is spent entirely on the problems worth solving. AI handles the routine. Engineers own the frontier.
No manual tagging. No surveys. No process changes. Connect your Jira or GitHub, and the data speaks for itself.
We ran task2vec on the full Spring Framework Jira archive: 69,156 tickets spanning 20 years of open-source development. The data reveals three tiers of AI readiness — and a trend that reframes the entire case for AI oversight.
AI automation doesn't shrink the escalate pile over time — it accelerates through the easy work and exposes the hard problems faster. By 2020 nearly 70% of new Spring tickets were architecture-level problems. The triage tool becomes more valuable as adoption grows, not less.
Paste any Jira ticket. We find the most similar Spring Framework tickets in our 61k dataset and look up how they actually resolved — to score yours as Automate, Assist, or Escalate in ~3 seconds.
All tickets projected into a shared 2D space. Proximity = semantic similarity. Cluster islands emerge naturally without any manual labelling.
Each contributor's work is divided into Early, Middle, and Recent thirds. Phase centroids on the map show where focus lived at each stage.
The gold arrow is the project's emergent strategy — the vector from the early centroid of all tickets to the recent centroid. No document defines it.
Cosine similarity between a contributor's personal trajectory and the project direction vector, mapped to a 0–100% alignment score.
A stacked bar chart showing which semantic clusters a contributor worked in each year — their thematic fingerprint over time.
An LLM-generated narrative summarising each contributor's career arc, written from structured data with no invented facts.
Interested in running this analysis on your own data, or want to talk about what task2vec could do for your team? Send us a message.