Intelligent
Error Tracking
Stop drowning in duplicate error reports. TigerOps groups, prioritizes, and explains every error across your entire stack — with AI that not only finds the bug but tells you exactly how to fix it.
TypeError: Cannot read properties of undefined (reading 'price')
src/components/Cart/CartItem.tsx:42
item.price in CartItem.tsx:42. The product prop can be undefined when the cart loads before the product API responds.NetworkError: Failed to fetch — POST /api/checkout
src/services/checkout.ts:89
UnhandledPromiseRejection: Redis ECONNREFUSED ::1:6379
src/cache/redisClient.ts:12
RangeError: Maximum call stack size exceeded in parseFilters()
src/utils/filters.ts:156
From Error to Fix, Faster Than Ever
Six capabilities that turn error noise into actionable, prioritized bug reports your team can actually resolve.
Smart Grouping
AI deduplication clusters similar errors regardless of variable values. See 10,000 occurrences as one issue, not 10,000 alerts.
Stack Trace Analysis
Full symbolicated stack traces with source context, local variable values, and highlighted frames from your own code.
Release Tracking
Automatically tag errors to the release that introduced them. See at a glance which version caused a regression.
Assignment & Workflow
Assign errors to team members, set priority, link to Jira/Linear tickets, and track resolution through built-in workflow states.
AI-Suggested Fixes
For every error, the AI analyzes your stack trace and codebase to generate a specific, actionable fix — not generic advice.
Source Map Support
Automatic source map upload from Webpack, Vite, or Rollup. See your original TypeScript source — never minified code.
Catch Errors Everywhere
Drop-in integrations with the tools your team already uses.
Frequently Asked Questions
How does AI-powered error grouping work?
TigerOps normalises each error by stripping variable parts from the message — such as user IDs, file paths, and memory addresses — and hashes the resulting stack trace fingerprint. Errors that share the same normalised fingerprint are grouped into a single issue regardless of how many times they occur. The AI additionally clusters issues that originate from the same root cause even when the error messages differ superficially.
What are AI-suggested fixes and how accurate are they?
For each unresolved error, the AI analyses the full symbolicated stack trace, the local variable values captured at the time of the error, and relevant code context to generate a specific fix recommendation. Accuracy is highest for common patterns like null pointer dereferences, missing null checks, and type mismatches. The suggestion always includes the exact file and line number to modify.
How do source maps work for minified JavaScript?
During your build process you upload source maps to TigerOps using the CLI or a Webpack/Vite plugin. When an error is captured from a browser, TigerOps automatically applies the source map to symbolicate the minified stack trace into your original TypeScript source with exact line numbers. Source maps are stored securely and never exposed to end users.
Can I track which release introduced an error?
Yes. You pass a release version string to the SDK at initialisation time. TigerOps tags every error with the release and shows a release timeline for each issue, making it immediately clear which deploy introduced or regressed a bug. The release comparison view shows the error rate change between consecutive releases across all issues.
How do I integrate error tracking with my existing Jira or Linear workflow?
TigerOps has native two-way integrations with Jira and Linear. You can manually create a ticket from any error issue with one click, or configure rules to automatically create tickets for errors above a certain occurrence threshold or user impact count. When the ticket is resolved in Jira or Linear, the error issue status is updated automatically.
Fix Bugs Before They Become Incidents
Start capturing, grouping, and resolving errors with AI-suggested fixes in under 5 minutes.
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