AI Evaluation & QA Testing Platform for Production AI Systems

Validate AI agents, test LLM workflows, and evaluate data pipelines before they reach production. Gen.QA gives engineering teams the infrastructure to ship AI applications with confidence.

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Core Platform Capabilities

End-to-end testing and evaluation infrastructure for every layer of your AI stack.

AI Agent Evaluation

Test autonomous agents across multi-step workflows, tool usage, and decision-making paths with deterministic and stochastic evaluation criteria.

LLM Workflow Testing

Validate prompt chains, RAG pipelines, function calling sequences, and multi-model orchestration with automated regression testing.

Model Performance Scoring

Evaluate accuracy, latency, cost efficiency, and output quality across model versions with configurable scoring dimensions.

Data Pipeline QA

Verify data ingestion, transformation, and training pipeline integrity with schema validation and output consistency checks.

AI Agent & LLM Testing Use Cases

From prototype validation to production monitoring, Gen.QA supports the full AI testing lifecycle.

Pre-Deployment Validation

Run AI agents through comprehensive test suites before shipping. Catch hallucinations, tool misuse, and edge-case failures in staging.

Regression Testing for Prompts

Track prompt changes across model versions. Detect output drift and quality degradation automatically when you update prompts or switch models.

Training Data Quality Assurance

Validate training datasets for completeness, bias, format consistency, and labeling accuracy before they enter your training pipeline.

RAG Pipeline Evaluation

Test retrieval accuracy, context window utilization, and answer grounding across your entire retrieval-augmented generation stack.

Multi-Agent Orchestration Testing

Validate agent handoffs, shared state management, and end-to-end task completion in multi-agent architectures.

Continuous Model Monitoring

Schedule recurring evaluations to track model performance over time. Get alerted when quality scores drop below your thresholds.

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How Gen.QA Compares to Manual AI Testing

Capability Manual Testing Gen.QA Platform
Agent Evaluation Ad-hoc scripts, inconsistent criteria Structured test suites with scoring dimensions
LLM Regression Testing Manual prompt comparison Automated diff across model versions
Data Pipeline Validation Spot-check samples Full schema + output consistency checks
Multi-Model Testing One model at a time Parallel evaluation across providers
Scheduling Cron jobs + custom tooling Built-in scheduling with threshold alerts
Reporting Spreadsheets Dashboards with historical trends

Data Pipeline & Model Evaluation Workflows

Gen.QA integrates into your existing ML infrastructure. Define evaluation criteria, run test suites, and track results across your pipeline stages.

01

Define Evaluation Criteria

Configure scoring dimensions, test personas, and acceptance thresholds for your AI system.

02

Create Test Suites

Build test cases that cover agent workflows, prompt chains, data transformations, and edge cases.

03

Run Evaluations

Execute tests on-demand or on a schedule. Gen.QA runs your AI system through each scenario and records results.

04

Analyze & Iterate

Review scores, identify failure patterns, and track improvements across runs and model versions.

Implementation Guidance

Get your AI testing infrastructure running with practical, step-by-step workflows.

Configure AI Agent Test Suites

Define test personas that simulate real user interactions with your AI agents. Each persona carries context, goals, and evaluation criteria that Gen.QA uses to score agent responses.

  • Multi-step conversation testing
  • Tool usage validation
  • Guardrail and safety checks
JSON 
// Example: Define an evaluation persona
{
  "persona": "data-engineer",
  "context": "Evaluating ETL pipeline agent",
  "goals": [
    "Extract data from source API",
    "Transform records to target schema",
    "Validate output completeness"
  ],
  "scoring": {
    "accuracy": 0.95,
    "completeness": 0.90,
    "latency_ms": 5000
  }
}

Schedule Recurring Evaluations

Run evaluations on a cron schedule to catch regressions early. Gen.QA tracks scores over time so you can correlate quality changes with prompt updates, model swaps, or data changes.

  • Cron-based scheduling
  • Threshold-based alerts
  • Historical trend analysis
JSON 
// Example: Schedule configuration
{
  "schedule": "0 6 * * *",
  "project": "prod-chat-agent",
  "test_suite": "regression-v2",
  "alert_threshold": {
    "accuracy": 0.90,
    "notify": ["slack:#ai-quality"]
  }
}

From the Blog

Technical guides, evaluation frameworks, and best practices for AI QA teams.

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Common Questions About AI Evaluation

What types of AI systems can Gen.QA evaluate?

Gen.QA supports evaluation of autonomous AI agents, LLM-powered applications, RAG pipelines, multi-model orchestration systems, and data processing pipelines. Any system that produces outputs from AI models can be tested.

How does Gen.QA handle non-deterministic AI outputs?

Gen.QA uses configurable scoring dimensions with threshold-based evaluation rather than exact-match testing. You define what "good" looks like across accuracy, completeness, safety, and custom criteria.

Can Gen.QA integrate with our existing CI/CD pipeline?

Yes. Gen.QA exposes event-driven APIs that integrate with GitHub Actions, GitLab CI, Jenkins, and other CI/CD platforms. Run evaluations as part of your deployment pipeline.

What is the difference between AI evaluation and traditional QA?

Traditional QA uses deterministic pass/fail assertions. AI evaluation uses multi-dimensional scoring to handle the probabilistic nature of AI outputs, including accuracy, relevance, safety, and consistency metrics.

Build Reliable AI Systems with Confidence

Gen.QA gives your engineering team the evaluation infrastructure to test, validate, and monitor AI agents, LLM workflows, and data pipelines at every stage.

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