Anomaly detection
Rules catch the failures you predicted. Anomaly detection covers the rest: the balance that doubled overnight, the segment that vanished, the ratio drifting past anything in its history. It runs upstream at the source, so a break is flagged before a report consumes it rather than after an examiner reads it.
Statistical detection runs across values, volumes, distributions, and relationships, baselined per dataset from its own history, with no thresholds to hand-set and maintain.
Placement is upstream. An anomaly surfaces at the source table with the downstream reports and CDEs already mapped, so you see the blast radius immediately.
Every flag arrives with its evidence, showing what changed, against which baseline, and in which loads, because a detection is a claim until a person rules on it.
Agents propose and humans decide. A flag is a proposed finding, and steward confirmation or dismissal feeds back to tune future detection.
Confirmed anomalies open incidents in the same workflow as rule failures, so there is one queue, one sealed record, and no side channel.
Learn
Models build a per-dataset baseline from history, covering values, volumes, distributions, and the relationships between them.
Detect
Each new load is scored against that baseline where the data lives.
Propose
An outlier becomes a proposed finding with its evidence attached. It is never treated as truth on its own.
Escalate
A confirmed finding opens an incident with the impact already mapped from the lineage graph.
“What catches the breaks nobody thought to write a rule for?”
Anomaly detection does. Statistical models baseline each dataset from its own history across values, volumes, distributions, and relationships, then score every new load against that baseline, with no thresholds to hand-set or maintain.
“Will this bury my stewards in false positives, or does it learn from what they dismiss?”
It learns. Every flag is a proposed finding, and steward confirmation or dismissal feeds back to tune future detection, so the signal sharpens as your team works the queue.
“Would we have caught this before it reached the regulatory report?”
You would, and upstream placement is the reason. Detection runs at the source table, so a break is flagged before a report consumes it, with the affected reports and CDEs already mapped so the blast radius is visible immediately.
Start with proof, not a promise.
There are three ways in, and each one earns the next. You approve the exit criteria before we spend any more of your time or budget.
A 60-minute working session
Bring one report that is expensive to prove. We walk the mechanism against it. Nothing is required from your side.
A two-week guided sandbox
Your team works hands-on against a synthetic sample bank. It requires zero access to your systems and zero procurement.
A six-week proof on your systems
We use read-only metadata across five systems and one priority report, scored against criteria both sides sign first. The decision it produces is yours.