Reliability engineering embedded into your architecture — load testing design, failure tolerance modeling, and observability built in from the start.
Designed systems handling 100K+ transactions/day at 99.99% uptime
Reliability can't be retrofitted. It is determined by architecture decisions made long before the incident that exposes them.
This engagement fits when:
Warning signs
SLA commitments without architecture to back them
Incidents that repeat with different symptoms
Monitoring that shows failures but doesn't prevent them
Enterprise clients asking about uptime guarantees
Realistic traffic modeling that surfaces architectural bottlenecks not just peak load thresholds, but the traffic shapes your users actually produce.
Every failure mode mapped with a designed response: isolate, degrade, alert, or handle silently.
Logging, metrics, tracing, and alerting designed as a coherent system so diagnosis takes minutes, not hours. For systems with AI components, this includes inference latency, model drift indicators, and token/cost telemetry.
Multi-region, database failover, and dependency resilience — the decisions that determine whether a component failure becomes a service outage.
We start from your SLA commitments and define what architecture and monitoring are required to hold them.
Questions we answer
Will this system maintain 99.99% uptime under these conditions?
What happens when a downstream dependency fails?
Are the SLOs designed into the architecture or aspirational?
What does graceful degradation look like for each service?
Our system includes AI components. How do we know if they're performing reliably in production?
Reliability is a design constraint, not a testing phase. By the time you're load testing, the structural decisions are already made.
We design failure tolerance into service interactions: circuit breakers that isolate faults, retry logic that won't cascade, graceful degradation that keeps core functionality running.
We define SLOs during architecture. An SLO the system wasn't designed around is aspirational. One that informed architecture, deployment, and testing decisions is operational.
How this differs
Reliability decisions made during architecture, before a line of code
SLOs that informed the design — not targets set after deployment
Failure modes mapped first, then load tested
Observability treated as architecture, owned by engineers
Yes. AI components introduce failure modes that standard observability misses like latency spikes, cost overruns, model drift etc. We treat them like any other critical dependency.
SRE (Site Reliability Engineering) is an ongoing operational role — someone who maintains reliability day-to-day. Reliability Engineering as a service is about designing reliability into the architecture: failure tolerance patterns, observability structure, SLO definitions. We build the foundation that your team operates on.
Both. Some clients need a reliability design they'll implement themselves. Others want us to build and configure the observability stack, set up alerting, and run the initial load tests. We scope based on what you need — design only, design and implementation, or a phased approach.
We're tool-agnostic. Datadog, New Relic, AWS CloudWatch, OpenTelemetry, Grafana/Prometheus — we work with what you have or recommend based on your stack and budget. The architecture matters more than the vendor. If you're starting fresh, we'll recommend options with trade-offs documented.
We start with what matters to your business and users: which failures cause churn, which latency thresholds trigger complaints, which processes have contractual commitments. Then we work backward to the technical metrics that predict those outcomes.
A senior architect will review your situation and recommend the right starting point.