3 AM, Production Down, Nobody Knows Why. AI Can Fix the On-Call Nightmare.
Wohlig Transformations · Platform Engineering
There is a specific kind of exhaustion that only on-call engineers know.
The pager goes off at 3 AM. The dashboard shows nine red squares. Five
Slack channels are filling up. The CEO has just messaged “what is
happening.” And you — half asleep, fingers on a keyboard — have to figure
out, from a graph that mostly looks the same as it did yesterday, which
of forty-three microservices broke first.
This is what observability looks like for most enterprises today. Tens of
millions spent on Datadog, New Relic, or Dynatrace. A wall of dashboards
nobody fully understands. Alert storms that train your engineers to
ignore alerts. And a Mean Time to Resolution that is measured in hours,
not minutes.
There is a better way, and Wohlig has been building it for customers who
are tired of paying premium SaaS prices for a problem the SaaS hasn’t
actually solved.
What AI changes about observability
The dirty secret of legacy APM is that it tells you what is broken but
almost never why. It shows you that latency spiked at 02:47. It does
not tell you which deploy caused it, which database query is hot, which
upstream dependency is down, or which pod is starved for memory. That
synthesis — the “why” — is exactly what your senior SRE does in their
head, and exactly what AI can now do in seconds.
A modern AI-powered observability platform does three things legacy APM
cannot:
1. It explains outages in plain English. When a service degrades,
the platform walks the dependency graph, correlates anomalies across
metrics, logs, traces, and profiles, and produces a narrative: “Latency
on checkout-service rose at 02:47. Root cause: a slow query in the
orders database, triggered by a deploy of inventory-service at 02:43
that introduced an N+1. Suggested action: roll back commit abc123.” In
Wohlig deployments, 80%+ of incidents are auto-explained the moment
the alert fires.
2. It collects telemetry without code changes. Instead of forcing
your developers to instrument every line, eBPF-based agents capture
HTTP, gRPC, Postgres, MySQL, Redis, Kafka, and MongoDB traffic at the
kernel level. Coverage is 100% on day one. There is no SDK to install,
no library to upgrade, no developer to convince.
3. It replaces alert storms with one intelligent alert per service.
SLO-driven alerting means you get paged once per service when something
that actually matters to users is breaking — not seventeen times because
seventeen thresholds tripped on the same root cause.
What this does to your numbers
Wohlig customers running an AI-powered observability stack see four
shifts that finance and engineering both notice:
MTTR drops by ~80%. Outages that used to last hours resolve in
minutes because the explanation is already on the alert.Observability bill drops 60–80%. Self-hosted, on commodity
hardware, replacing per-host SaaS pricing.Cloud waste drops 15–30%. Continuous profiling shows which
workloads are over-provisioned and which queries are burning CPU.On-call burnout drops sharply. Junior engineers can resolve
incidents that previously required a senior. Pager fatigue eases.
Retention improves.
These are not hypothetical. They are the numbers from real Wohlig
deployments.
“But our data must stay inside our network”
For most banks, insurers, healthcare operators, government agencies,
and regulated enterprises, the largest single observability cost is not
the SaaS bill — it is the inability to use a cloud SaaS at all.
Telemetry contains customer identifiers, transaction details, internal
IPs, and infrastructure secrets. Sending it to a third-party tenant is
a non-starter.
Wohlig deploys the entire AI observability stack — agents, storage,
analysis, UI, AI root-cause engine — inside the customer’s own VPC. Data
never leaves the perimeter. The AI inferencing can be pointed at a
customer-controlled model endpoint. Cloudflare-grade security posture,
without giving your data to a third party.
Who this is for
Mid-to-large enterprises running Kubernetes or microservices in
BFSI, e-commerce, SaaS, healthtech, logistics.Digital-native businesses where every minute of downtime costs
measurable revenue.Regulated industries that need self-hosted observability for data
sovereignty.CTOs and VPs Engineering sitting on a six-figure Datadog or New
Relic renewal and wondering why they pay so much for so little
insight.
Where Wohlig fits
Wohlig has migrated 20+ workloads to cloud-native architectures. We
know the eBPF, Kubernetes, OpenTelemetry, Prometheus, and ClickHouse
stack at depth — and we know what it takes to operate it in
production at the kind of scale Indian consumer platforms run on.
We design the deployment for your environment, integrate it with your
existing Prometheus, OpenTelemetry collectors, and identity stack,
configure SLOs for the services that matter, train your SRE team, and
help you decommission the legacy SaaS contract on a timeline that does
not break the business.
The honest summary
The next time the pager goes off at 3 AM, your on-call engineer
should not be a detective. They should be a reviewer — confirming the
fix the platform already proposed, rolling back the deploy the
platform already identified. That is what AI does to observability,
and it is what Wohlig builds.
Wohlig Transformations builds AI, cloud, and data platforms for
governments, enterprises, and high-growth startups. 20+ cloud
migrations delivered. 40+ Google Cloud certifications. Founded 2016.
Offices in India and London.