Industries
AI Platform Testing
QA for products that are the AI platform: prompts, tools, retrieval, evals, and the app around the model — not a foundation-model bake-off.
The problem
An “AI platform” is still software. It has tenants, billing, APIs, and a model-backed surface that can fail fluently. Testing only the chat widget misses the platform. Testing only the platform misses the answers.
This page is the industry view. The workstream lives under AI Quality. We do not benchmark foundation models against each other.
What’s at risk
- Eval sets that do not exist, so every prompt change is a hope
- Agents that call tools with bad arguments — AI agent testing
- RAG that answers from the wrong document — RAG evaluation
- Guardrails that wrap the UI and not the API — guardrail testing
- Conventional SaaS bugs (roles, billing) next to the model
What we test
- LLM evaluation against your definition of correct
- Agents, RAG, chatbots, red teaming, hallucination — as scoped
- The product around the model: functional, API, tenant rules
- Regression after prompt, model, or index changes
How we work
Same models as Services. Many AI platform teams start with a scoped QA audit of the evaluation habit, then staff QA as a service around a model upgrade.
Deliverables
- Scenario or eval sets you can re-run
- Findings on answers, tools, retrieval, and safety — plus ordinary product defects
- A written “re-run when X changes” list
FAQs
Is this the same as AI testing services?
AI testing services is the parent workstream. This page is for buyers who searched “AI platform testing.” Use the AI Quality hub to pick a specialist page.
Do you certify our model?
No.
Talk to our QA team
Tell us the product and the industry risk
We will say which engagement model and test types fit — without a canned industry pack or invented case-study numbers.