RECOMMENDATIONS

Your next best fix, already written up.

Observability tools show you problems. Obsivara hands you the fix. The Recommendations feed converts every signal — failures, drift, waste, risk — into concrete actions ranked by impact: "Switch this node to a smaller model — benchmark shows quality parity at lower cost" or "Roll back prompt v18, it added 28% latency."

Ask Obsivara
Why is my support workflow slower this week?
Root cause chain
Prompt v18 deployed Tue 09:48
└→+2,200 tokens per call
└→latency +28% across 3 nodes
Rollback to v17 →
WITHOUT IT

The problems that pile up quietly.

Dashboards show a hundred metrics but never say what to actually do.

Optimization work stalls because investigating each opportunity takes hours.

Junior engineers can’t act on raw telemetry; the fixes bottleneck on one senior person.

WITH OBSIVARA

What changes for your team.

Impact-ranked, evidence-attached

Every recommendation states its expected effect in dollars, latency, or reliability, and links to the traces that prove it. You start at the top and work down.

Fixes anyone can ship

Each item includes the specific change — the model to switch, the prompt version to roll back, the retry setting to cap — so the whole team can execute, not just the expert.

A compounding backlog of wins

Accepted recommendations are tracked to outcome. You get a running record: 23 fixes shipped, $4,100/month saved, error rate down 40%.

HOW IT WORKS

Live in minutes, not sprints.

1

Observe everything

Executions, prompts, spend, credentials, and changes all feed the recommendation engine.

2

Generate and rank

Detected issues become recommendations, scored by expected impact and effort, deduplicated, and refreshed as conditions change.

3

Act and verify

Accept a recommendation, ship the change, and Obsivara verifies the effect landed — closing the loop with before/after numbers.

ranked
recommendations ordered by impact, not noise
specific
each fix names the model, prompt, or setting to change
100%
of recommendations backed by trace evidence

Questions

Alerts tell you something is wrong; Recommendations tell you exactly what to change. Each item states the specific model to switch, prompt version to roll back, or retry cap to set — with the expected impact in dollars, latency, or reliability points, and a link to the traces that prove it.

They are ranked by expected impact and effort, so you always start with the fix that moves the needle most. Low-signal findings are suppressed until they become significant enough to act on.

Yes — once you accept a recommendation and ship the change, Obsivara tracks before/after metrics and confirms whether the effect landed, closing the loop with evidence rather than assumption.

See it on your own AI stack.

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