DevOps & Cloud · Incident timeline
IncidentLens
Clarity in the first ten minutes.
When production is down, see when it started, what changed just before, and the log lines that prove it, from the logs you already have.
$ incident-lens --logs api.jsonl checkout.log --alerts alerts.json --deploys deploys.csv
incident from 2026-10-12 10:03 UTC (error spike in the logs); errors/min 0.11 before → 10.56 during
10:01:30 deploy: Deploy v2.4.1 (api)
10:03:00 first new error in api: sqlalchemy.exc.TimeoutError: QueuePool limit reached
1. [78] Deploy v2.4.1 of api, 2 min before the start
2. [75] Database: connections, locks or schema
3. [73] A dependency is down or unreachable
The problem
In the first ten minutes of an outage, people scroll through thousands of error lines while the real cause, a deploy two minutes earlier, sits in another tool.
What it does
Reads JSON-lines, plain-text and access logs, with stack traces folded in
Finds the start of the incident from the error spike or the first alert
Groups thousands of error lines into a few types and spots the new ones
Ranks likely causes: recent deploys, database, dependencies, timeouts, config, credentials, certificates
Every cause shows its score, evidence and the log lines behind it
Reads Alertmanager, PagerDuty and Opsgenie exports and deploy CSVs
Optional Claude brief gets redacted summaries only, never raw logs
How it works
- Export the logs, alerts and deploys around the outage
- Read the timeline and the ranked causes
- Paste the Markdown into the incident channel or send the HTML report
Works with
Your data
- Runs where your logs are.
- Nothing is shipped elsewhere.
- Timelines are ready for the postmortem.
Who it's for
Questions
Do we need a log platform?
No. It works on the files you already have.
How does it find the cause?
It finds when error patterns started and lines them up with deploys and configuration changes just before.
Related
Often used with IncidentLens
BlastRadar
Change impact analysisKnow what a change will touch. Before a release, see every service a change can affect and the line of code that links it. After the release, confirm those services are still healthy.
$300–600 (₹10,000–20,000 in India)View product → DevOps & CloudSLOStudio
SLOs and error budgetsReliability you can measure. Define each SLO in a few lines and get correct burn-rate alerts, an error budget everyone understands, and a release gate, on the monitoring you already run.
$1,500–3,000 (₹40,000–80,000 in India)View product → DevOps & CloudTestScope
Test impact selectionRun the tests that matter. Every pull request runs the tests its change can affect, each with a stated reason, and the full suite whenever a change is too broad to trust.
$1,000–2,500 (₹25,000–60,000 in India)View product →See IncidentLens on your own data
A 30-minute call. We run the product on your export, repository or document and walk you through the results.