KALYR

Adaptive Intelligence for Live Operations

Every incident strengthens the next decision.

Kalyr by EmbreierAdaptive incident intelligence
Production reliabilityDecision support inside live operations
Demonstration environment

Turn every incident intostronger operational capability.

Every action and outcomebecomes context for the next decision.

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One incident. Intelligence matched to the moment.

Explore Kalyr

Playing

Explain

OPS CONSOLEPAY-APISEV-1replication · eu-west-1 · shard 3
14:22:063 ON CALLSHIFT B
Replication lag0.6srising
Error rate2.4%+1.8 pt
Pool utilisation94%near limit
Deploys today14last 14:18
Lag, 30 min
Service topology14 services · 3 degraded
edgegwauthpay-apiledgerriskcachequeuereplica-areplica-bdc-euobs
KALYR
Checked · pool saturation confirmed

Replication lag increased after the deploy.

Next

Check replica connection saturation before rollback.

Check poolEscalate

Deploy history · DB telemetry · 12 comparable incidents

Why this

Lag increased four minutes after the deploy. Storage latency was already ruled out. Two comparable incidents were connection saturation.

Still uncertain

A connection pool reset was not recorded.

Context used

Deploy history. Database telemetry. Outcomes of relevant prior incidents.

Signal feedlive
TimeSourceSignalBy
14:21:30pay-apino change after retryops-2
14:20:55replica-apool utilisation 94%auto
14:20:11storagelatency nominalauto
14:19:40replica-areplication lag 0.6sauto
14:18:02pay-apideploy 7f3a91 completedci
14:11:47queueconsumer lag clearedauto
Streampay-api · warn+

14:21:31 WARN repl.apply lag_seconds=0.62 shard=3

14:21:30 INFO retry.mgr attempt=3 result=no_change

14:20:56 WARN pool.pg in_use=188/200 waiters=21

14:20:11 INFO storage p99_ms=4.1 nominal

14:19:41 WARN repl.apply lag_seconds=0.58 shard=3

14:18:03 INFO deploy sha=7f3a91 status=complete

14:18:02 INFO deploy sha=7f3a91 rollout=100%

█

ABC
AOne move, inside the tool they already use
BTheir signals, untouched
CThe reasoning is one click away

Kalyr enters where the decision happens, inside the tools your team already uses.

More of the team closing more of the work.

Same incident. Different responder.Scale expert judgment across the whole team.

Kalyr gives each responder what they need to make the next decision well.

Responder A

Replication lag increased after the deploy.

Storage latency is nominal and the connection pool is near its limit. Two comparable incidents resolved at the pool. Next: check replica connection saturation before rollback. Explain · reasoning and a next check
Responder B

Replication lag after deploy.

Storage ruled out. Connection saturation is the live branch. Nudge · the anomaly and the branch

Every recurring incident, covered by the whole rotation.

Start with one recurring incident class your best responders already know how to resolve.

See Kalyr on your incident class
/0.1Replication lag after deployStorage looks nominal, the pool is saturated, and only two people reach for the pool first.
/0.2Certificate and secret expiryThe runbook exists. Knowing which of nine services fails first does not.
/0.3Queue backpressureThe queue is the symptom. The consumer that stalled is the answer, and it is different every time.
/0.4Cache stampede on restartEveryone knows to warm it. Almost nobody knows the order.
/0.5Partial region failureHalf the signals are lying, because half the region is gone.
/0.6Capacity saturation under retryCaused by the retries themselves. The obvious remedy deepens it.
Measured on independence

Resolve more incidentswith the team you already have.

Expand independent resolution

More of the rotation resolves this incident class without senior rescue.

3 → 5Responders resolving it independently

Free your senior experts

Critical expertise moves from default escalation path to the moments that truly require it.

DownEscalations landing on the same engineers

Accelerate readiness

Responders reach independent resolution faster across recurring incident classes.

ShorterWeeks to a first independent resolution

Grow coverage without sacrificing reliability

Support adapts as capability develops while operational performance remains the constraint.

HeldTime to mitigate across the class

Same headcount. More operational capability. Expertise compounds instead of resetting.

Illustrative · demonstration data

Start with one recurring incident.

/0.1See Kalyr on your incident class

Bring one recurring workflow your best people already know. We replay the decision path against the evidence available at the time.

 /0.2Explore the decision system

See how state, responder context, authority, uncertainty and outcomes shape each intervention.

Interface shown is a demonstration environment. Figures are illustrative.