Sales fell 14%. Zmist found why in nine minutes.
Connect data from GitHub, Jira, Prometheus, spreadsheets, and public sources. Zmist normalizes it into a shared timeline of signals, then explains what changed, why it may matter, and what to investigate next — before you open five dashboards.
One pipeline, three layers
Source → Mapping → Signal → Channel → Analysis → Report → Decision.
01
Collect
CSV, Excel, Google Sheets, APIs, webhooks, monitoring systems, SaaS integrations, public data, or a message typed by hand. Every source is an entry point, not the product.
02
Understand
Every source maps into one normalized signal model — timestamped, labeled, sourced. Machine data and human comments live on the same timeline, in the same channel.
03
Explain
Zmist compares periods, detects anomalies, suggests questions, and writes a grounded report across one channel or many — with evidence and a stated confidence level, never a bare claim.
Ask across GitHub, Jira and your logs at once
The strongest first case: an engineering incident, three tools, one question. Zmist gives an answer with evidence — not a guess.
What most likely contributed to the production incident?
Release v2.8.4 deployed at 14:30. HTTP 500s rose 8.2× within eleven minutes, concentrated in the checkout service — the same service touched by three merged PRs in that release. Most suspicious: PR #482, checkout validation refactor.
Read the full report →
Live public data, explained
Every public channel is a real demo: data, a chart, a grounded explanation, and a source. See what Zmist does before you connect anything of your own.
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GitHub Release Activity
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