Test enterprise AI applications before hallucinations, retrieval failures, and unsafe tool calls reach production. 

Enterprise AI has moved from experimentation to production. LLMs now answer customer questions, summarize internal knowledge, reason across workflows, and trigger actions through connected tools. But the testing model most teams rely on has not kept up. 

Traditional QA validates deterministic software. AI systems are different. They depend on prompts, models, embeddings, vector indexes, APIs, context servers, permissions, and tool calls that can drift independently after deployment. A single regression in any layer can produce a fluent, confident, and incorrect output. 

QyrusAI closes that gap with a unified AI testing platform built for LLM evaluation, RAG pipeline testing, MCP endpoint validation, API testing, and continuous production monitoring. 

Where AI Testing in 2026 Falls Short 

Most AI teams use separate tools for separate problems: one tool for prompt evaluation, another for RAG metrics, another for API testing, and another for red-teaming. That fragmentation creates blind spots where real production failures happen. 

• LLM evaluation misses retrieval failures when the model scores well against the context it received, even if the retriever supplied the wrong context. 

• RAG testing misses infrastructure drift when API schema changes, stale indexes, or failed ingestion jobs silently degrade retrieval quality. 

• MCP testing misses agent risk when tool calls, permissions, authentication, and session state are not validated as part of the AI workflow. 

The result: teams pass pre-release evaluations, ship confidently, and still discover hallucinations, outdated answers, tool misuse, data leakage, or workflow failures in production. 

One Platform for the Full AI Quality Stack 

QyrusAI brings AI quality signals into one continuous testing loop. Instead of treating API testing, LLM testing, RAG evaluation, and MCP validation as disconnected workflows, QyrusAI correlates them in a single platform so teams can see what failed, where it failed, and what changed before the failure appeared.

What QyrusAI Helps You Test 

LLM Evaluation 

Measure groundedness, faithfulness, hallucination risk, bias, toxicity, response relevance, and instruction following. 

QyrusAI helps teams move beyond basic prompt checks by evaluating whether model responses are accurate, safe, relevant, and aligned with business expectations. 

RAG Pipeline Testing 

Validate retrieval precision, context recall, chunking strategy, index freshness, reranking quality, and generation accuracy. 

RAG systems fail when the wrong context is retrieved, when indexes become stale, or when the model generates answers that are not faithful to source data. QyrusAI helps identify whether the issue is in retrieval, generation, or the infrastructure that connects them. 

MCP Testing 

Test tool definitions, tool call correctness, authentication propagation, session isolation, output schemas, and context server integrity. 

As enterprises adopt MCP-enabled AI agents, tool-calling becomes a critical production risk. QyrusAI helps validate whether agents are calling the right tools, passing the right parameters, respecting permissions, and handling tool outputs safely. 

API Infrastructure Testing 

Validate REST, SOAP, GraphQL, gRPC, and WebSocket endpoints that power AI workflows. 

AI quality depends on the systems underneath it. If an API changes, fails, slows down, or returns malformed data, AI outputs can degrade even when the model itself appears healthy. 

Production Drift Monitoring 

Detect model, prompt, retrieval, index, schema, and tool changes before they become user-facing failures. 

QyrusAI helps teams continuously monitor AI systems after deployment, so silent drift does not turn into hallucinations, compliance issues, or customer-facing incidents.