LLM & AI Application Testing
AI features do not fail like ordinary software: the same input can produce different output, and a model or prompt change can quietly degrade quality. We build the evaluation harness that tells you, with numbers, whether your AI product is getting better or worse.
What we cover
- Evaluation datasets built from your real use cases and failure reports
- Automated evals for correctness, groundedness, relevance and tone
- RAG testing: retrieval quality, citation accuracy and hallucination rate
- AI agent testing: tool-call correctness, multi-step task completion and recovery
- Adversarial testing and red teaming: prompt injection, jailbreaks and data leakage
- Regression evals in CI for every prompt, model or retrieval change
- Latency and cost-per-request tracking alongside quality
- Guardrail verification and human-review workflows for edge cases
From first call to running tests
- 1
Discovery call
We look at your product, stack, current tests and release process, and agree on the outcome you need.
- 2
Audit & plan
You get a short written plan: what to automate first, the framework and pipeline design, and a fixed scope.
- 3
Build & integrate
We build the suite and pipeline in your repo in short iterations, with tests running in your CI early on.
- 4
Handover or run
We train your team and hand over, or stay on to maintain and extend the suite as your product grows.
LLM & AI Application Testing: common questions
How do you test something non-deterministic?
By measuring instead of asserting exact output. We build a representative dataset, score responses with a mix of deterministic checks, model-graded rubrics and human review, and then track those scores across versions. A release is gated on thresholds rather than on one exact string.
Which AI products do you test?
Customer-facing chatbots and copilots, retrieval-augmented generation (RAG) systems, AI agents that call tools or APIs, and classic ML features embedded in applications.
Can you test for prompt injection and data leakage?
Yes. Adversarial testing is part of the service: we probe for prompt injection, jailbreaks, system-prompt and data leakage, and unsafe tool use, and turn every finding into a regression test.
Often combined with
AI Test Automation
AI-assisted test generation, self-healing locators and agentic testing, reviewed by engineers and committed as plain code you own.
Playwright, Playwright MCP, Cypress
QA AI Pipelines & CI/CD
We design and integrate testing pipelines: parallel runs, smart test selection, flaky-test triage, AI failure analysis and quality gates on every pull request.
GitHub Actions, GitLab CI, Jenkins
API & Backend Automation
REST, GraphQL and gRPC test suites, contract testing between microservices, and database and event-driven validation.
Pytest, REST Assured, Postman
Tell us what you want to stop testing by hand
A few lines about your product, stack and biggest quality problem are enough. We will reply by email, usually within one business day, to set up a discovery call.
- No obligation, no sales script
- A concrete plan and a fixed scope
- NDA available on request
Or talk to us directly