Workload modeling
Derive journeys, arrival patterns, concurrency, pacing, and stages from evidence.
Performance Engineering
Model demand, validate resilience, and explain bottlenecks.
Combine NFRs, API collections, HAR files, logs, topology, and telemetry to create defensible workload models and performance evidence.
5supported framework directions
P50-P99.9percentile-aware analysis
End-to-endmodel to remediation lineage
Capabilities
A complete working system, not an isolated generator.
NFR validation
Check targets against supplied traffic and architecture evidence.
Script generation
Build correlated, parameterized, threshold-aware performance assets.
Distributed execution
Coordinate safe, isolated load generation with environment guardrails.
Bottleneck detection
Correlate latency, errors, saturation, logs, metrics, and traces.
Capacity guidance
Translate observed limits into evidence-backed scaling decisions.
Operating flow
Every stage is visible and reviewable.
Inputs, decisions, evidence, findings, approvals, and artifacts remain available throughout the workflow.
Evidence
Normalize NFRs, HAR, logs, collections, and topology.
Model
Create baseline, peak, stress, and degradation profiles.
Generate
Build scripts, data, correlation, and thresholds.
Critique
Validate safety, pacing, workload fidelity, and assertions.
Execute
Run approved workloads with observability attached.
Analyze
Explain breaches and prioritize remediation.
How it fits
Connected to the wider quality lifecycle.
This capability works with shared project context, accountable decisions, and traceable evidence from source through outcome.
01Evidence intake: NFRs, HAR, API collections, logs, and traffic samples.
02Workload agent: Models demand and marks unsupported assumptions.
03Script agent: Generates and critiques framework-specific assets.
01Evidence intake
NFRs, HAR, API collections, logs, and traffic samples.
02Workload agent
Models demand and marks unsupported assumptions.
03Script agent
Generates and critiques framework-specific assets.
04Execution fabric
Distributed load generation with safety controls.
05Engineering analysis
Telemetry correlation and remediation evidence.
Engineering outcomes
What this changes for delivery teams.
Workloads linked to real demand evidence
Safety controls before load reaches an environment
Faster bottleneck investigation
Capacity decisions grounded in repeatable runs
Working session
Explore Performance Engineering with your own delivery context.
Bring one requirement set, workflow, or performance concern. We will map the governed path from source to executable evidence.