Testing of AI
Validating the non-deterministic. We provide the independent assurance layer to ensure your intelligent systems are accurate, secure, and compliant.
TESTAI
AI isn't tested with traditional pass/fail logic. We use probabilistic validation and human-directed Adversarial Testing to audit Agent behavior, quantify hallucination rates, and verify that agentic workflows operate within strictly defined safety guardrails.
Ask for a demoTESTAI – Context-Aware AI Validation Capabilities
- Adversarial Testing
- Bias, Hallucination and PII Validation
- Agentic Guardrail Verification
- LLM Benchmarking
AI Assurance Tests
Specialized validation layers for the LLM and Agentic ecosystem.
LLM Benchmarking
Evaluating model performance against custom datasets to ensure accuracy, tone, and reliability.
Agentic Behavior
Verifying that autonomous agents follow business logic and hand off tasks without failure.
Adversarial Testing
Probabilistic testing to discover jailbreaks, prompt injections, and security vulnerabilities.
RAG Accuracy
Auditing the retrieval pipeline to ensure AI responses are grounded in your private enterprise data.
Bias & Fairness
Quantifying model bias and ensuring equitable outputs across diverse user demographics.
Governance Audits
Preparing technical documentation for regulatory compliance (EU AI Act, etc.) and safety logs.
RELEASING GENAI APPS WITH CONFIDENCE
BUILDING A MULTI-STAGE ASSURANCE PIPELINE FOR LLM RELIABILITY
The client was deploying high-stakes generative AI features to millions of users. We built a Multi-Stage Assurance Pipeline that combined automated adversarial testing with human-in-the-loop expert validation, ensuring every model update met strict safety and brand guidelines before production release.
By combining RAGAS evaluation metrics with LangChain-driven automation, we provided the technical safety net required to scale GenAI with absolute confidence. This framework bridged the gap between "experimental" AI and "enterprise-grade" reliability.
AI Testing Services.
Book a 45-minute AI validation session to review model risks and define your assurance roadmap.
Book AI Assurance ReviewSafety Insights
Deep dives into the probabilistic nature of AI testing and model trust.
Rethinking AI Test Generation: The Role of Continuous Learning
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The RAG Triad in Practice: Faithfulness, Context Relevance & Answer Relevance with RAGAS
Part of the AI Testing series, our earlier blog Engineering Trust: The Mandate for Testing Agentic AI & RAG, introduced the RAG triad as a…
Why AI Test Generation Fails to Scale: Solving the 40% Accuracy Plateau
Introduction AI test generation is rapidly reshaping how organizations approach software quality. While what once required deep expertise and significant manual effort is now being…
