LLM Testing & Validation
LLM & RAG Testing Experts, On-Demand
Specialists in validating large language models and retrieval-augmented generation
systems with measurable quality, safety, and reliability.
Comprehensive LLM Testing Features
Our advanced testing suite covers every aspect of LLM validation from accuracy to safety
Accuracy & Hallucination Testing
Factual accuracy validation against ground truth Hallucination detection & quantification Context relevance and consistency testing Framework tags: NIST AI RMF, ISO/IEC 23053
Model Validation
Comprehensive testing of GPT-4, Claude, Liama, and custom LLMs for accuracy, consistency, and performance.
Model Validation
Comprehensive testing of GPT-4, Claude, Liama, and custom LLMs for accuracy, consistency, and performance.
Model Validation
Comprehensive testing of GPT-4, Claude, Liama, and custom LLMs for accuracy, consistency, and performance.
Model Validation
Comprehensive testing of GPT-4, Claude, Liama, and custom LLMs for accuracy, consistency, and performance.
Model Validation
Comprehensive testing of GPT-4, Claude, Liama, and custom LLMs for accuracy, consistency, and performance.
Why Choose Our LLM & RAG Testing
Three pillars of excellence that ensure your Al systems are production-ready
Deeper Coverage
Validate retrieval accuracy, context relevance, and output faithfulness with comprehensive testing methodologies.
Measurable Confidence
Leverage 20+ metrics to evaluate hallucinations, toxicity, and bias with quantifiable results.
Future-Ready Assurance
Ensure Al systems are safe, trustworthy, and production-ready for enterprise deployment. Tweak
Our Testing Toolkit
Industry-leading frameworks, metrics, and technologies for comprehensive LLM & RAG
validation
Evaluation Frameworks
DeepEval
RAGAS
LangSmith
TruLens
OpenAl Evals
Key Metrics
Relevance
Faithfulness
Hallucination Rate
Toxicity
Bias
Factuality
Diversity
Latency
Cost
Testing Areas & Methodologies
Comprehensive testing across four critical areas of LLM validation
Accuracy & Reliability
- Factual accuracy validation
- Consistency across multiple runs
- Edge case handling
- Error rate measurement
Accuracy & Reliability
- Factual accuracy validation
- Consistency across multiple runs
- Edge case handling
- Error rate measurement
Accuracy & Reliability
- Factual accuracy validation
- Consistency across multiple runs
- Edge case handling
- Error rate measurement
Accuracy & Reliability
- Factual accuracy validation
- Consistency across multiple runs
- Edge case handling
- Error rate measurement
Supporting Technologies
Vector Databases
Pinecone, Weaviate, Milvus
Orchestration
LangChain, LangGraph
Agents & MCP
Model Context Protocol
How It Works
Our proven 4-step process for comprehensive LLM & RAG testing
01
Define Test Objectives
Identify specific quality, safety, and performance requirements for your LLM & RAG systems.
02
Apply Frameworks
Deploy industry-leading evaluation frameworks tailored to your use case and requirements.
03
Collect Metrics
Gather comprehensive data across 20+ metrics including accuracy, safety, and performance.
04
Report & Improve
Deliver actionable insights and recommendations for system optimization and deployment.
Trusted by Production Teams
Used by teams adopting RAG for production-critical systems.
Faster Test
More Test Creation
Coverage
Fewer Flaky Regression Tests
Ready to validate your LLM & RAG systems with confidence?
Get comprehensive testing that ensures your Al systems are safe, reliable, and production-ready.