RAG Eval for teams moving AI into production
We measure the RAG Triad—Faithfulness, Relevance, and Contextual Precision before your users do.
There’s no faster way, identifying the right tool needs strategy and evaluation
Start with battle-tested presets, then define your own. Each parameter links to tests, datasets, and acceptance thresholds your stakeholders agree on.
Factual Accuracy
Catch hallucinations fast. Checks answers against your docs and data, verifies citations, and flags unsupported claims.
Instruction Accuracy
Ensure models follow your rules. Tests adherence to system prompts, tone, and exact format requirements.
Reasoning Depth
Measure real thinking. Evaluates multi-step logic, math accuracy, and quality of explanations.
Latency & Cost
Balance speed and spend. Tracks response time, token usage, and cost per completed task to optimize economics.
Tool & Agent Reliability
Test real-world actions. Validates correct API calls, error recovery, and reliable handoffs in agent workflows.
Safety & Compliance
Ship securely. Scans for PII leaks, policy violations, and prompt injection resistance before production.
Get Clear Metrics: Built to Catch What API Tests Miss
Tailored for Every Role
Address role-specific challenges in retrieval, grounding, and hallucination detection while ensuring a unified, evidence-based testing approach for reliable AI agents.
SDETs
Streamline RAG testing with automated faithfulness, grounding, and contextual precision checks. Import your retrieval pipelines and documents instantly. Run AI-driven evaluations that catch unsupported claims and retrieval drift — all inside your existing test workflows.
QA Teams
Eliminate manual review and fragmented tools. Get real-time grounding scores, hallucination diagnostics, citation fidelity reports, and collaborative dashboards. Reduce test cycles from days to hours while ensuring every AI response is traceable to your source documents.
Product Managers
Catch business-critical hallucinations and grounding failures early. Gain clear visibility into relevance, faithfulness, and contextual precision so you can make data-driven decisions and confidently ship AI features that users can trust.
Integration Specialists
Validate complex RAG workflows across systems without dependencies. Automatically detect context drift, citation mismatches, and retrieval failures. Scale effortlessly with pay-as-you-go evaluation that ensures seamless integration between your knowledge base and AI agents.
Startups
Move fast without compromising trust. Deliver reliable AI agents without a large QA team. QAPI’s RAG testing automatically validates grounding, relevance, and hallucination risk so you can ship confidently and iterate quickly.
Freelancers & Solo Developers
Every minute counts when you’re building alone. Automate repetitive grounding and faithfulness checks, catch hallucinations before they reach users, and ensure your RAG pipelines stay reliable.
They talk about it better than us
QAPI simplified our API testing with its ability to test individual API's and API Chaining for functional and performance testing. QAPI's AI integration capability allows recording of API's with API Discovery extension and generation of API assertions which is of great help and improved our time to market. Definitely recommend to try it.
Venkata Satya Prasad Sajja
Principle Architect
The qAPI service stands out for its exceptional ease of use, reporting, and comprehensive testing capabilities. With its embedded AI features, instead of writing complex test scripts from scratch, qAPI generated tests based on the API specifications and endpoints, making it incredibly easy for our technical and non-technical users to get started with automated testing. Additionally, with the growing demand for high-performance APIs, using qAPI's Performance Testing and Load Simulation features gave us the ability to test scalability and handle high traffic without compromising on reliability.
Nicholas Rios
Test Architect
As a small startup, finding an affordable API testing solution was crucial for us. qAPI not only fits our budget but also offers a robust cloud-based platform that scales with our needs. We can now perform extensive testing without the overhead costs associated with traditional solutions.
Peter K
Software Developer
Frequently Asked Questions
An LLM evaluator helps teams compare, score, and analyze large language models using structured parameters and business-specific criteria so they can determine which model is most suitable for a real use case
Custom parameter design matters because standard model benchmarks do not always reflect the specific quality checks, compliance expectations, tone controls, or workflow requirements a business needs before deployment.
It is intended for product, QA, engineering, innovation, AI, and governance teams that need a structured and repeatable way to compare LLMs before making a recommendation or rollout decision. No B.S just actionable results.
Manual testing is often inconsistent and hard to compare across models. LLM Evaluator is positioned as a way to make evaluation more systematic, measurable, and aligned to business needs.