AI Quality
AI testing services for products that can fail fluently
Nexus Eclipse tests how your product uses models — answers, tool calls, retrieval, and safety — not whether the model vendor’s demo looked good. This silo is the AI quality workstream; engagement models live under Services.
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AI Testing Services
Practical QA for conversational AI, LLM workflows, and AI agents — prompt quality, hallucination detection, tool-call validation, and regression after every model or prompt change.
Specialist pages
Pick the failure mode, not a generic AI QA label
“AI testing services” is the category query. Buyers usually need one of the jobs below. Use the parent page if you want the whole workstream staffed; use a specialist page if you already know the risk.
LLM Evaluation Services
Evaluate LLM outputs against a definition of correct for your product — instruction following, format, factual checks, and version-to-version comparison after prompt or model changes.
AI Agent Testing
Test AI agents on tool calls, actions, and multi-step tasks — the right tool, valid arguments, recovery when a tool fails, and regression after you change the agent graph.
RAG Evaluation Services
Evaluate retrieval-augmented generation: did we fetch the right passages, ground the answer, cite honestly, and refuse when the corpus has no answer?
Chatbot Testing Services
Test chatbots and conversational flows: multi-turn context, fallbacks, voice or chat UX, and regressions after you change intents, prompts, or the containment path.
AI Red Teaming
Adversarial testing of AI products: jailbreaks, prompt injection, data exfiltration, and policy bypass — so you learn how the system fails before an attacker or a bored user does.
Guardrail Testing
Test the safety and policy filters you already deployed — do they fire, do they over-block, and do they still work after a prompt or model change?
Hallucination Testing
Probe AI products for fabricated facts, invented sources, and confident wrong answers — with human review where there is no cheap ground truth.
AI-Powered Test Automation
Use AI inside the test process — generating candidates, triaging flakes, and drafting checks — without pretending a model can replace a maintained automation suite.
AI-Generated Code Audit
Review code a model wrote before it ships: correctness, missing tests, unsafe defaults, and the gaps autocomplete will not mention.
What AI testing services should mean
A conventional test can pass while the product is wrong. The API returned 200. The chat widget rendered. The answer was fluent. None of that tells you whether the model invented a source, called a tool with a bad argument, or retrieved the wrong document.
AI testing services, as we sell them, are QA for those failure modes inside your product. We do not benchmark foundation models against each other. We do not sell a “certified AI tester” badge. We design scenarios, run them, and report findings you can act on — the same discipline as the rest of our software testing services, aimed at generative surfaces.
This hub exists so “AI testing services” has a home that is not buried under a generic services menu. The migrated parent page isAI testing services. Older URLs at/services/ai-testing/ 301 here so existing links and Search Console history keep working.
How the specialist pages differ
LLM evaluation is about scoring and comparing outputs against a definition of correct for your use case.AI agent testing is about tool calls, actions, and multi-step tasks.RAG evaluation is retrieval and grounding.Chatbot testing is conversation paths, context, and voice or chat UX.
AI red teaming is adversarial: jailbreaks, abuse, and policy bypass.Guardrail testing checks the filters you already think are on.Hallucination testing is fabricated facts and invented citations.AI-powered test automation is different: using AI inside the test process, not testing an AI product.AI-generated code audit is review of code a model wrote, before it ships.
How this sits next to engagement models
You can buy AI quality work asQA outsourcing, adedicated QA team seat,QA as a service around a model change, or a scoped QA audit of the AI workstream only. The silo describes the work. Services describes how we plug into your team.
Book a QA consultation with the product URL and the next prompt or model change on the calendar.
FAQs
What are AI testing services?
AI testing services are structured QA for products that use models: chatbots, agents, RAG, and LLM workflows. The work measures answers, tool calls, retrieval, and safety — not only whether the page loaded.
Do you benchmark foundation models?
No. We test how your product uses a model: prompts, retrieval, tools, and the user-facing flow. Model-vendor bake-offs are out of scope.
How is this different from functional testing?
Functional testing still matters for login, payments, and UI. AI quality work is extra because a fluent wrong answer can pass a conventional test. See AI testing services for the parent offer, then the specialist pages for agents, RAG, red teaming, and the rest.
Can this sit inside QA outsourcing or a dedicated team?
Yes. Engagement models live under Services. AI Quality is the workstream those models can include.
Talk to our QA team
Tell us which AI surface is about to change
We will say whether you need the full AI testing workstream or one specialist page — and which engagement model fits.