Paste a sample
Provide a representative raw JSON log in the ingestion workbench.
All use cases
Raw event in, reviewable pipeline out
Onboard unfamiliar logs with AI assistance
Turn a representative JSON event into a reviewable Doris schema and collector configuration without handing AI uncontrolled production access.
coreva_logslogs_application_events1H
search service, level or message...⌘ K
Log streamAI
TIMELEVELSERVICEMESSAGE
10:42:18.904ERRORcheckout-apipayment upstream timeout · trace=8fa2
10:42:18.887WARNpayment-svcretry budget at 80% · region=tr-1
10:42:17.512INFOgatewayrequest routed · latency=34ms
Explain this error cluster with AI→
A new service emits useful events, but nobody wants to spend another sprint choosing types, indexes, retention and shipper syntax.
AI should remove blank-page work—not remove operational control.
The pain
The data exists; the pipeline does not
01
Fields and types must be inferred manually.
02
Schema drift makes rigid mappings fragile.
03
Collector configuration differs by source.
04
Unsafe automation creates more risk than it removes.
The UnifyLogs path
AI proposes. Your team decides.
Review the proposal
AI returns table DDL and a collector configuration in editable code blocks.
Deploy deliberately
An authorized user reviews and explicitly triggers table creation and a test event.
What changes
The operating pattern changes—not only the tool.
- Faster first ingestion for custom formats
- Reviewable output instead of opaque automation
- Flexible handling for evolving JSON attributes
CONTACT / HUBSPOT
Bring us the log problem you have today.
Tell us where your logs live, what breaks during incidents, and what must remain on-prem. We will respond with a practical evaluation path.