You can't fix what you can't see
Most teams have monitoring. Few have observability. The difference is whether you can debug a novel failure at 2 AM without deploying new code. We build observability platforms that give your engineers full visibility — metrics, logs, and traces — unified, correlated, and actionable.
Three pillars. One unified picture.
Metrics, logs, and traces each answer a different question. Alone, they give you fragments. Together, correlated by trace ID and timestamp, they give you the complete story of every request through your system.
Metrics
Know what's happening right now
Metrics are the quantitative pulse of your infrastructure. CPU saturation, request latency, error rates, queue depth — numeric signals that tell you whether your systems are healthy or heading toward failure. Without metrics, you're running production blind.
Tool Ecosystem
What It Reveals
- Resource saturation before it causes outages
- Latency trends across services and endpoints
- Error rate spikes correlated with deployments
- Capacity forecasting for infrastructure planning
Logs
Understand why it happened
Logs are the narrative record of what your systems did and why. Structured logs, correlated across services, turn a wall of text into a searchable timeline. When metrics tell you something is wrong, logs tell you what went wrong — the stack trace, the failed query, the malformed payload.
Tool Ecosystem
What It Reveals
- Root cause of errors with full stack traces
- Audit trails for compliance and security
- Cross-service request correlation
- Anomalous patterns in application behavior
Traces
Follow the request end-to-end
Distributed tracing maps the full journey of a request as it crosses service boundaries. When a checkout request is slow, tracing shows you exactly where the 400ms latency lives — the downstream API call, the database query, the serialization step. Without traces, debugging microservices is guesswork.
Tool Ecosystem
What It Reveals
- Latency bottlenecks across service boundaries
- Dependency maps generated from real traffic
- Failed spans pinpointing exact failure points
- Performance regression detection per deployment
Alerts should inform, not exhaust
Alert fatigue is an engineering crisis. When your on-call engineer receives 500 alerts per day, they stop reading them. The real incident gets buried in noise — and customers find the outage before your team does. We fix this.
The Alert Fatigue Problem
500+ alerts per day
Team ignores all of them
No severity tiers
Everything is P1, nothing is P1
Symptom-based alerts only
Woken up for non-issues at 3 AM
No runbooks attached
On-call engineer guesses what to do
Static thresholds
False positives during traffic spikes
The Structured Alerting Approach
Tiered severity model
P1 means real customer impact — nothing else
SLO-based alerting
Alert on error budget burn rate, not raw metrics
Alert routing and escalation
Right person, right channel, right context
Runbook automation
Every alert links to resolution steps
Dynamic thresholds
Baselines adjust to traffic patterns automatically
Measure reliability the way your customers experience it
Service Level Objectives turn vague uptime promises into measurable contracts. An SLI measures a real user experience signal. An SLO sets the target. An error budget tells you how much failure you can tolerate before your users notice. This is how Google, Netflix, and every serious platform team manages reliability.
SLI
Service Level Indicator
The metric you measure. Successful requests divided by total requests. P99 latency under a threshold. The quantitative signal of user experience.
SLO
Service Level Objective
The target you commit to. "99.9% of checkout requests succeed within 300ms over a 30-day window." Concrete, measurable, and tied to business impact.
Error Budget
Acceptable Failure
The inverse of your SLO. At 99.9%, you have a 0.1% error budget — roughly 43 minutes per month. Spend it on deployments, experiments, or incidents.
Tools we deploy and operate
We're tool-agnostic. Whether you run open-source Prometheus and Grafana or enterprise Datadog and Dynatrace, we configure, tune, and integrate it into a cohesive observability platform. We pick what fits your scale, your budget, and your team's capacity to operate.
Prometheus
Metrics
Grafana
Visualization
Datadog
Full Stack
Dynatrace
APM
Jaeger
Tracing
OpenTelemetry
Instrumentation
Loki
Log Aggregation
PagerDuty
Incident Mgmt
E-commerce platform reduced MTTR from 4 hours to 12 minutes
E-Commerce & Retail
The Challenge
A mid-market e-commerce platform serving 2M monthly active users had no centralized observability. Every incident required SSH-ing into individual pods, tailing logs manually, and correlating timestamps across six microservices by hand. Mean-time-to-resolution was four hours on a good day. During their holiday sale, a payment processing failure went undetected for 47 minutes — costing an estimated $380K in lost transactions.
Our Approach
We deployed a unified observability stack: Prometheus and Grafana for metrics, Loki for centralized log aggregation, and Tempo with OpenTelemetry for distributed tracing. We instrumented all six core services, built SLO dashboards for checkout, search, and payment flows, and implemented tiered alerting with PagerDuty integration. Every alert was tied to a runbook.
Results
12 min
Mean-time-to-resolution
95%
Fewer false-positive alerts
47→0
Minutes of undetected outages
$380K
Revenue loss prevented
Frequently asked questions
Technology Partners
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