Your quality tool found the anomaly.
It can't tell you why.
Quality alerts tell you something broke. Then an engineer hops across five tools to find out why. Unravel lives where the answer is — your code, your compute, your pipelines — and Arvix resolves the incident, not just reports it.
Watch the data. Diagnose the cause. Resolve it.
Other tools stop at the alert. Unravel correlates, explains, and fixes — autonomously when you let it.
One alert. One workload view. One platform deciding what to do.
A quality alert today kicks off a 40-minute scavenger hunt. With Unravel, the alert opens an incident view that already has the answer.
Your AI is only as good as the data feeding it.
Stale training data, drifting features, RAG sources gone light — these don't break loudly. They quietly degrade the model until the answer is wrong. Unravel watches the data behind every AI workload.
Stale training data
RAG retrieval gaps
Distribution drift
Keep what works. Make it smarter.
Standardized on Great Expectations? Heavy in Informatica? dbt tests, Soda, custom SQL? All of it flows in.
One incident. Every signal. One platform deciding what to do about it.
They watch the data. They don't see the system around it.
The cause of a quality incident is almost never in the table. It's upstream — in the code, the compute, the config that ran the night before.
No performance correlation
They see the row drop. They can't see the failed Spark stage or OOM that caused it. Unravel does.
No cost correlation
They tell you the data's wrong. Not what the bad pipeline cost — or what the backfill will. Unravel quantifies both.
Alert. Don't fix.
They open Slack threads and Jira tickets.Arvix proposes and applies the fix — code, config, or compute.
Single-platform coverage
Snowflake-only. Warehouse-only. Unravel covers Databricks, Snowflake, and BigQuery in one platform.
Three incidents. Same playbook.
The same correlated diagnosis works whether the broken thing is a finance dashboard or an AI training pipeline.
