Functional Test Automation
Validate web, mobile, API, integration, and end-to-end workflows.
SERVICE / SOFTWARE TESTING
Build confidence before production.
We test complete systems: interfaces, APIs, integrations, data, retrieval, prompts, model outputs, tool execution, permissions, performance, and failure behavior.
AI introduces variable behavior, but quality is still engineerable through representative datasets, layered evaluations, deterministic controls, monitoring, and disciplined release processes.
WORKFLOW
CASES
EXECUTE
MEASURE
ANALYZE
RELEASE
CEREBRIXSYSTEM
01 / SYSTEM VIEW
The model is one component. The operating system around it creates dependability.
02 / WHAT WE BUILD
Capabilities designed around real operating requirements.
Agent Evaluation
Test tool selection, permissions, retries, memory, escalation, and task completion.
RAG Evaluation
Measure retrieval relevance, groundedness, citations, completeness, and failure behavior.
Model & Prompt Testing
Compare versions, identify regressions, score task quality, and analyze failure categories.
Performance & Reliability
Assess latency, concurrency, resilience, dependency failures, and recovery.
Release Validation
Provide risk-based coverage, findings, evidence, and a release-readiness assessment.
03 / CORE CAPABILITIES
Engineering depth across the complete solution.
- Test strategy
- UI automation
- API testing
- Integration testing
- Evaluation datasets
- LLM-as-judge calibration
- Human review rubrics
- Prompt regression
- RAG metrics
- Agent simulation
- Performance testing
- CI integration
- Defect analysis
- Quality dashboards
04 / REPRESENTATIVE USE CASES
[ Release validation ][ Legacy regression automation ][ Agent red-team scenarios ][ RAG quality baselines ][ Prompt comparison ][ Tool failure simulation ][ Permission testing ][ Production quality monitoring ]
05 / DELIVERY APPROACH
Structured delivery without unnecessary ceremony.
Model the risk
Identify critical workflows, quality attributes, failure modes, users, and operating conditions.
Design coverage
Combine deterministic tests, evaluation data, simulation, human review, and monitoring.
Automate evidence
Integrate repeatable tests and evaluations into development and release workflows.
Analyze failures
Group defects and model failures by cause, severity, frequency, and user impact.
Improve continuously
Turn production feedback and incidents into new regression coverage.
06 / QUALITY + CONTROL
AI quality requires more than one score.
We evaluate the software, retrieval, prompts, models, tools, permissions, workflow completion, and human experience as connected layers.
LET'S DEFINE THE RIGHT SYSTEM
Build confidence before production.
We can assess an application, create an AI evaluation framework, or establish a complete quality-engineering strategy.