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AI Testing

Make sure your AI is accurate, safe and reliable

Independent testing for AI and LLM-powered products, plus AI-assisted testing that makes your existing QA faster. Quality is where TRAN started.

What we build

AI testing services

LLM & chatbot evaluation

Measure accuracy, relevance and hallucination rate against real-world test sets.

Safety & red-teaming

Test for prompt injection, data leakage, harmful output and policy violations.

Regression testing for AI

Automated suites that catch quality drops when prompts, models or data change.

Agent & workflow testing

End-to-end testing of AI agents that call tools and complete multi-step tasks.

Performance & cost testing

Latency, throughput and cost-per-request testing under realistic load.

AI-assisted QA

Use AI to generate test cases, test data and analyse failures for faster testing.

How it works

Our approach

01

Risk analysis

Identify what can go wrong and what matters most to your users.

02

Test set design

Build evaluation datasets and scoring rules from real scenarios.

03

Automated evaluation

Run the tests on every change in your CI/CD pipeline.

04

Report & improve

Clear quality reports with concrete fixes.

What you get

Deliverables

  • AI test strategy and risk assessment
  • Evaluation datasets and scoring
  • Automated AI regression suite
  • Safety and red-team report
  • Quality dashboard per release
Technology

Tools we use

LLM evaluation frameworksPythonPytestPlaywrightPostmanCI/CDPrompt testingLoad testing tools

We choose technology to fit your stack, budget and data-privacy requirements, including self-hosted options.

FAQ

Frequently asked questions

Why does AI need special testing?

AI output varies and can be wrong in subtle ways. It needs evaluation datasets, scoring and continuous regression testing, not just traditional pass/fail test cases.

Can you test an AI product built by another team?

Yes. Independent testing is our core business. We give an unbiased view of quality before release.

What is red-teaming?

Red-teaming means deliberately trying to make the AI misbehave, for example leaking data or ignoring rules, so the problems are fixed before attackers or users find them.

How does AI help normal software testing?

AI can draft test cases from requirements, generate test data, maintain UI locators and group failures, which saves significant QA time.

Let’s talk

Test your AI before your users do

Tell us about your goals. We’ll reply within one business day with ideas and a proposed next step.