Crash reports, in-app screenshots, log anomalies, and support tickets pulled into one queue, clustered by an agent, with predictive signals that surface friction before users report it.
Crash data, user screenshots, infrastructure anomalies, and support tickets all arrived, but in four separate systems with no shared severity model. The same underlying defect was counted as a dozen separate issues, and triage was done by whoever happened to look first.
We built a single ingestion path that normalises every source into one issue shape, then let an agent cluster, score, and prioritise the queue. Confirmed failures and predictive signals are kept visually distinct, because a crash you have seen and a pattern you have inferred need different urgency.
Screenshot from the running project.
Crash reports, SDK screenshots, log anomalies, and tracker tickets are normalised into one queue, so the same defect stops appearing as a dozen separate items.
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Every report carries source, priority, status, and occurrence counters, and the queue filters by date window, source, and priority, so triage starts from the narrowest question instead of a spreadsheet.
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Each crash becomes a structured report with a confidence score, an executive summary, the exact throwing location, the affected device models, and an impact breakdown, read straight from the crash pipeline rather than assembled by hand.
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The analysis resolves to the failing call site and shows the before and after diff, what changed and why, plus an ordered implementation plan with a time estimate on every step.
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The same report quantifies what happens if the fix is skipped, from crash rate and users affected to revenue impact and support load, and what the fix buys back, so priority gets argued with numbers.
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A copilot agent answers questions about any issue in plain language, streaming its reading of root cause, blast radius, and recommended remediation right beside the report.
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Reactive failures and inferred patterns are kept apart in every view, so a crash you observed is never confused with a signal you merely inferred.
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Crashlytics, the mobile SDK, Elastic, and Jira connect as first-class sources, and each integration reports its own sync state and record count.
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CSV crash uploads, ClickUp sync, the mobile SDK, and Elasticsearch log import are configured from one place, so a new source never needs a code change.
Screenshot from the running projectOne queue replaced four, and the agent's clustering cut a noisy stream into work that can actually be assigned. The predictive view now flags friction patterns weeks before they surface as a drop in conversion or a spike in support volume.
Most teams already have the data. It is just spread across four places that do not talk to each other: crash reports from the build, screenshots and signals from the SDK, log anomalies from the infrastructure, and tickets from the tracker. Every one of them looks urgent on its own, and none of them knows how many of the other reports it actually explains.
OneTrace puts them in a single queue and treats that queue as the product. An agent reads each incoming report, groups the ones that share a root cause, scores their sentiment and severity, and pushes the group forward as one item with one owner, instead of a hundred near-duplicates.
The predictive half matters more than the reactive half. Screenshots and SDK signals are collected before a user knows anything is wrong, so patterns surface as friction - a payment button that is pressed and never confirms, a checkout that is abandoned twice in a row - rather than as a crash report filed tomorrow. That is the difference between reacting to a failure and noticing a pattern while it is still cheap to fix.