AI Platform

Intelligence at the Core of Everything

Ruviq's AI engine is the shared intelligence layer powering PerfTestFlow and Qualixy. It learns from every test run, deployment, and defect to get smarter over time.

AI Models

Specialized Models for Every Challenge

Purpose-built AI models trained on engineering-specific data for maximum accuracy.

📈

Performance Predictor

97%

LSTM-based model trained on 10M+ performance data points to forecast regressions.

🔍

Defect Classifier

94%

Transformer model that clusters and categorizes defects with root cause attribution.

⚙️

Test Generator

92%

Code-aware LLM that creates comprehensive test scenarios from requirements and code.

⚠️

Anomaly Detector

99%

Unsupervised learning model for real-time detection of performance and quality anomalies.

Capabilities

What the AI Engine Does

📈

Predictive Performance Analysis

Anticipate bottlenecks and failures before they occur using ML models trained on real traffic patterns.

🔍

Intelligent Defect Insights

AI clusters and triages defects automatically, surfacing patterns human reviewers miss.

⚙️

Automated Test Optimization

Continuously refines test suites based on code changes, risk signals, and historical outcomes.

🧠

Root Cause Analysis

Deep-learning diagnostics trace failures to their origin across distributed microservice architectures.

Real-Time Engineering Intelligence

Live dashboards aggregate performance, quality, and risk signals into a unified engineering view.

🔄

Adaptive Learning Engine

Platform learns from every test run, improving accuracy and reducing noise over time.

AI Assistant

Conversational Engineering Intelligence

Ask complex engineering questions in plain English and get instant, actionable answers.

AI

Ruviq AI Engine

Analyzing your codebase

GPT-4oLive

Analyze the latest performance test results for the checkout service.

I detected a p99 latency spike of 340ms on the /checkout/confirm endpoint during peak load (2,400 rps). Root cause: N+1 database queries in the order validation layer. Recommendation: implement batch query optimization — estimated improvement: 78% latency reduction.

P99 SpikeRoot Cause FoundFix Ready

Generate optimized load test scenarios from the HAR file.

Analyzed 8,420 real user transactions. Generated 6 JMeter test scenarios covering 94% of traffic patterns. Parameterized 23 dynamic values. Test plan is ready for CI/CD integration.

8.4K Requests6 ScenariosCI/CD Ready
Ask Ruviq AI anything about your engineering data...
Integrations

Works with Your Existing Stack

Ruviq connects seamlessly with the tools your engineering team already uses.

GitHub
GitLab
Jenkins
CircleCI
JIRA
Confluence
Datadog
Grafana
AWS
GCP
Azure
Kubernetes

Experience AI-Powered Engineering

See the Ruviq AI engine in action with your own data and workflows.

Request a Demo