Stop Firefighting.Start Preventing.

Every finding, fix, approval and recurrence linked into one causal graph — so you can trace why an action ran and what it resolved, instead of correlating tabs by hand.

Per-check effectiveness from your own history: which action actually resolved this before, how often it worked, and the median time it took — surfaced before you act.

For each active SEV1/SEV2 finding, a forward-looking call on who resolves it — autonomy or a DBA — with probability and confidence drawn from matching past patterns.

Every resource trending toward its limit — tablespace, backup area (FRA), memory (PLE), sessions — ranked by what needs attention first, each with the chain that follows if ignored: cause → effect → business outcome. Plain English, not raw math.

Before an action runs — or a resource crosses its limit — see exactly what else is in range: the apps, standbys and downstream databases it touches, scored by impact, condition and dependency, with a plain “why this score” behind every number.

For a live finding, the past times the same check fired on this or a topologically-similar database — ranked so a fix that demonstrably worked floats to the top, next to what it means and the recommended action.

When a database is degraded, PulseDBAi traces the shared-fate graph to the most likely origin — or honestly says the database is the origin itself — with a plain-English confidence chip, never a raw score.

Every finding carries what it is worth in plain money — dollars-per-hour of exposure per database and a per-incident cost, rolled up across the estate — so the biggest risk is the most expensive one, not just the loudest.

When the graph proves a downstream alert is only a consequence of an upstream fault, PulseDBAi silences the noise and attaches it to the real cause — so you get one root-cause alert, not fifty symptoms.

A side-by-side projection of autonomy ON vs OFF: the hours saved across the fleet, and — when DBA rates are configured — the cost. The value of autonomy, quantified.

A per-check runbook composed from real history — Detect, Recommend, Approve, Verify, Rollback, Escalate — so the right response is documented, not improvised.

Did the fix actually solve it? Each action class is scored on whether the problem stayed gone — closing the loop on decision quality, not just execution.

One page ranks every database by what needs attention first — tablespace, backup area (FRA), memory, and connection capacity — in plain English, led by a single “Needs attention: N” count. Not raw math.

For every resource forecast to cross its limit, PulseDBAi shows the cause → effect → outcome chain — e.g. “backup area fills in ~18h → the archiver stalls → the database halts, with no recovery point.” See what breaks before it does.

Forecasts SQL Server memory-life (PLE) collapse and Oracle session exhaustion days ahead, on the same timeline as capacity — so pressure is caught while there is still time to act.