Your system under pressure - before your users apply it.
Performance issues don’t show up in unit tests. They don’t show up in staging. They show up during your biggest demo, at peak traffic, or the moment an AI feature that ran fine locally turns out to be an inference cost catastrophe at scale. LEO stress-tests your systems before any of that happens – with load, endurance, and observability built in from the start.
The Problem
Performance Failures Always Arrive at the Worst Time
Most teams don’t test performance until something breaks in production. By then, the damage is done – in SLA penalties, customer churn, emergency engineering sprints, and the reputational cost of an outage during a high-visibility moment. AI systems add a new dimension: a feature that passes functional tests can still silently destroy your infrastructure bill or degrade over time as model behavior shifts.
What LEO Delivers
Performance Assurance Across Every Layer
- Load testing — validate system behavior under expected and peak concurrent user volumes
- Stress testing — push systems beyond normal capacity to find the breaking point
- Spike testing — simulate sudden traffic surges to validate auto-scaling and recovery
- Soak/endurance testing — run sustained load over hours to detect memory leaks and gradual degradation
- AI inference benchmarking — latency, throughput, and cost profiles for LLM-powered features at scale
- RAG pipeline performance — retrieval latency, embedding throughput, end-to-end response time
- Performance regression gates — automated checks that block deployments that degrade performance baselines
- Cloud cost modelling — understand the performance-cost tradeoff for AI workloads before they hit production
How It Works
LEO's Process
Performance Baseline
LEO profiles your current system — response times, throughput, error rates, and resource utilization under normal conditions. This baseline is the benchmark everything is measured against.
Scenario Design
Load scenarios are designed around real traffic patterns: peak hours, batch jobs, concurrent API calls, and growth projections. AI workloads get dedicated inference stress scenarios.
Execution & Analysis
LEO runs the full test battery against your staging environment. Bottlenecks are identified, failure thresholds are documented, and findings are ranked by severity and business impact.
Regression Integration
Performance gates are integrated into your CI/CD pipeline. Every deployment is automatically checked against baselines – no regression ships to production undetected.
Best for
This Agent Is Right For You If...
- You're approaching a major launch, enterprise demo, or high-traffic event
- You're deploying AI inference at scale and need to understand cost and latency before it hits production
- Your SaaS contracts include performance SLAs you need to validate before signing
- You've had a production performance incident and need to prevent the next one
- You're building for regulated industries (BFSI, Healthcare) where performance requirements are compliance requirements
Ready To Work Together?
Stress Test Your System With LEO
Tell us your highest-risk performance scenario — peak traffic event, AI feature launch, or enterprise SLA milestone. LEO will design a testing program that gives you confidence before it counts.
