Reliability Engineering
Fixed once, stays fixed; ready for the next failure
Makes sure a problem fixed once stays fixed.
For VP Engineering, Head of SRE
What changes
Stop raw incident counts. Start seeing it live.
Makes sure a problem fixed once stays fixed, and prepares services for failures that haven't happened yet. It verifies root causes and tracks whether they recur; puts every critical user journey on a service-level objective with a live error budget; tests the main failure modes of critical services and verifies recovery; and forecasts saturation of compute, storage, connections and quotas well ahead of time.
Before · reports you wait for
- raw incident countsreport
- uptime percentages on monthly reportsreport
- the annual disaster-recovery drillreport
- threshold alerts at 90% utilisationreport
After · live on DevSemantic
- RPR Repeat Prevention RateFixed problems that stay fixed95%↑
- EBA Error Budget AdherenceCritical services within their error budget92%↑
- FMC Failure Mode CoverageFailure modes tested with recovery verified84%↑
How it works
DevSemantic is a context plane for site reliability and operations. It connects telemetry, services, dependencies, releases and runbooks into one model, so problems are predicted from leading signals, handled by approved automation, and prevented from returning. The four solutions follow the life of a production problem: see it clearly, catch it before it pages, stop it coming back, and stop releases from introducing it.
We model how production really runs
Services, dependencies, releases, telemetry and runbooks in one live model.
We catch the first warning sign
Problems are raised early, often before a user notices or anyone is paged.
Approved playbooks act first
Engineers step in with the evidence already gathered. Every production change still needs a human yes.
Fixed once stays fixed
Root causes are tracked so they do not come back. Releases are checked before they deploy.
What we are measured on
One headline number. Three that back it up.
Fixed problems that stay fixed
Share of verified root causes that do not recur in the review period after their fix ships
95%
your baseline · 48 %+47 pts in 90 days
Commitment · Up against baseline; the same cause does not returnEvidence · Incident history
-
EBAError Budget AdherenceSupporting
Critical services within their error budget
Share of critical services that end the review period within their error budget
92%↑ vs baseline
Replaces uptime percentages on a monthly reportEvidence · Customer's telemetry
-
FMCFailure Mode CoverageSupporting
Failure modes tested with recovery verified
Share of critical services whose main failure modes (dependency loss, zone outage, saturation, bad config) were tested in the period, with recovery verified against target
84%↑ vs baseline
Replaces the annual disaster-recovery drillEvidence · Experiment records
-
SFLSaturation Forecast LeadSupporting
Days of warning before something saturates
Days of warning between a capacity forecast and the resource reaching saturation, across compute, storage, connections and quotas
21days↑ vs baseline
Replaces threshold alerts at 90% utilisationEvidence · Incident history
Numbers shown are illustrative. Yours start from your own baseline, measured in the diagnostic.
Proof you can open
Every number comes from a record you own.
Here that record is the incident history. If we cannot show where a number came from, we do not report it.
- day 1 · 09:04rcaroot cause verified · stale DNS cache on svc-auth · fix shipped r-3391
- day 2 · 11:21trackreview period: no recurrence · RPR sample recorded
- day 4 · 13:38slojourney checkout · budget 99.9% · 62% remaining
- day 5 · 15:55experimentzone outage test · svc-orders · recovery 04:10 vs target 05:00 · pass
- day 7 · 17:12forecastrds-connections saturates in 19 days · ticket opened
Questions
What people ask before they start.
Does an AI decide the root cause?
Agents assemble and correlate the evidence and propose a cause. Your pod and your engineers verify it against the evidence before it is recorded.
Who writes the fix?
Your pod, with your developers, in your repositories and review process.
How do you know a fix worked?
When the cause does not return in the review period. Until then, the fix is shipped but not verified.
What if the evidence is not there?
The analysis says so. A missing log or metric is reported as UNKNOWN, and closing the gap becomes part of the work.
See Reliability Engineering on your own estate.
A 30-minute walkthrough with an engineer. We show the RPR loop running and answer what it would look like for you.
Every solution is sold against one headline KPI, committed for 90 days against your own baseline and reported from evidence you can inspect.
vikat.AI · your guide
Ask Yati
Which solution fits your estate, what the 30-day diagnostic measures, how a pod works inside your team: ask in your own words.