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Does this ticket need a human?

Paste any Jira ticket below. We find the most similar Spring Framework tickets in our 61k-ticket dataset and look up how they actually resolved — how fast, how watched, by whom — to score yours.

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Confidence
Most similar historical tickets
Key Summary Match Resolved in Tier
How the score is calculated
Semantic similarity — your ticket is embedded and matched against 61,000 Spring Framework tickets using cosine similarity on text-embedding-3-large vectors.
Outcome signals — each matching ticket is labelled by how it actually resolved: resolution time, watcher count, and assignee experience. Fast + low-watch + junior = Automate; slow + high-watch + senior = Escalate.
Weighted vote — the final probabilities are a similarity-weighted average of the labels of the top-10 matching tickets that have resolved outcome records.

This model is trained on Spring Framework data.

Your team's tickets are different. A model calibrated on your own Jira history — your resolution times, your engineers, your complexity patterns — gives you a personalised AI readiness score for every ticket you assign.

Run this on your own data →