About Latencio

Load test verdicts,AI RCA next.

Latencio turns raw load test output into a verdict with evidence. Today that means faster performance analysis. Tomorrow it becomes the AI RCA layer behind release confidence.

Right now
Performance verdict engine
live
What we automate

Upload JMeter, k6, or similar results.

Cross-reference Prometheus, CloudWatch, New Relic, and logs.

Return a conservative PASS, WARN, or FAIL with evidence and fix direction.

< 60s
Time to verdict
3
Signal layers
4+1
Analysis stages
1
Root-cause narrative
INPUTSENGINE · 5 PHASESOUTPUTSL1 · LOADJMeter / k6 / GatlingL2 · INFRAPrometheus / CloudWatchL3 · APMNew Relic / LogsP1 · Statistical profilingP2 · Threshold detectionP3 · Pattern recognitionP4 · Cross-signal correlationP5 · Root cause rankingVERDICTPASS / WARN / FAILFINDINGSRanked by impactREPORTEngineer + stakeholder
Why this exists

Analysis is the slow part.

Engineers still spend hours bouncing between listeners, Grafana dashboards, APM waterfalls, and logs just to answer one question: is this safe to ship?

Hours of dashboard archaeology

Every run becomes a manual correlation exercise across separate tools and timelines.

2–4 hours today

Weak regression confidence

A raw P99 diff does not tell you whether the change is real, noisy, or root-cause-worthy.

Need evidence, not charts

Stakeholder translation gap

Managers need risk and release readiness, not just latency charts and throughput tables.

One analysis, two audiences

The engine

Deterministic first. AI-ready by design.

We do not want a black-box analyzer. Latencio is built on explicit phases, auditable rules, and evidence-backed synthesis so the future AI RCA layer has something trustworthy to stand on.

P1Signal ingest

Canonicalize load results, map services, and establish analysis windows.

P2Cross-source correlation

Bring infrastructure, APM, and logs onto the same timeline.

P3Rule engine

Evaluate deterministic bottleneck and symptom rules with confidence controls.

P4Ranking suspects

Promote only the causes that are actually corroborated.

P5Verdict synthesis

Return PASS, WARN, or FAIL plus the narrative and fixes.

Signal model

Three layers. One root-cause story.

Load tells you the symptom. Infra shows where stress built. APM and logs explain why.

L1Load test results

JMeter · k6 · Gatling · Locust

What was slow?

Per-request latency, error rate, throughput, and concurrency behavior.
Endpoint and service-level symptoms surfaced from the run itself.
The baseline truth for whether the system held under tested load.
L2Infrastructure metrics

Prometheus · CloudWatch

Where was the stress?

CPU, memory, GC, thread pool, network, and container stress signals.
Capacity ceilings and starvation patterns during the measured window.
Proof that the problem was environmental, systemic, or absent.
L3APM traces and logs

New Relic · Datadog · Jaeger · Loki

Why did it happen?

Slow DB spans, downstream calls, app code hotspots, and trace timing.
Error logs, pool exhaustion, timeouts, and stack traces as confirmation.
The evidence that elevates a symptom into a supported root cause.
Where this goes

From verdicts to RCA.

Today we analyze performance runs and the services involved in them. The same direction grows toward cross-service RCA, incident-grade diagnosis, and trustworthy AI explanation.

Upload results and get a verdict.
Compare runs and isolate regressions.
See the services involved and why they mattered.
Move toward a world-first AI RCA engine with evidence discipline built in.
Start with a load testCompare two runsConnect observability