AI product teams
Teams shipping chat interfaces, copilots, AI search, document assistants, AI image workflows, or embedded LLM features.
AI QA Agency
AI QA for LLM products, agents, and RAG systems.
Qualura is a senior-led AI QA agency for teams building LLM products, AI agents, RAG systems, copilots, and AI-powered workflows. We test the failures traditional QA usually misses: hallucinations, grounding gaps, unsafe behavior, state drift, prompt injection, broken tool use, and silent workflow failures.
What an AI QA agency should actually test
AI QA is not only checking whether buttons work. An AI product can look polished while the model invents facts, ignores retrieved context, leaks sensitive data, chooses the wrong tool, or gives a confident answer based on a false premise.
Qualura combines exploratory AI testing, LLM behavior evaluation, safety testing, grounding validation, workflow testing, mobile testing, and classic QA discipline. The output is evidence your product team can act on before users, investors, or enterprise buyers find the issues themselves.
This page is the main overview of how we test AI products, who we help, what proof we publish, and how the 5-Day AI Risk Audit Sprint works.
Who Qualura helps
We are built for teams where AI behavior is part of the product promise, not a side feature.
Agent builders
Teams building AI agents that use tools, memory, approvals, retries, permissions, or multi-step workflows.
RAG and knowledge products
Teams whose product depends on retrieval quality, source grounding, citations, uploaded documents, or enterprise knowledge bases.
Pre-launch founders
Founders preparing for launch, investor review, enterprise pilot, procurement, or a public release where reliability matters.
Internal QA teams
QA and engineering teams that already cover normal functional testing but need AI-specific risk discovery.
Complex SaaS teams
SaaS teams adding AI workflows where silent failure, incorrect output, or broken state can damage user trust.
Core AI QA coverage
Focused services for teams that need evidence about real AI behavior, not generic QA theater.
LLM behavior testing
Prompt adherence, refusal quality, tone drift, consistency across reruns, missing-context handling, and model behavior under realistic user pressure.
Grounding and hallucination testing
Whether responses are supported by retrieved context, uploaded files, images, documents, citations, or the actual message payload.
AI agent testing
Tool selection, memory, state transitions, retry behavior, permissions, approval gates, and multi-step task completion.
AI safety testing
Unsafe outputs, jailbreak behavior, harmful transformations, prompt injection, data leakage, and inconsistent guardrail behavior.
Mobile AI workflow testing
Real user flows across Android, iOS, browser, share sheets, file upload paths, voice input, orientation changes, and device-state changes.
Evidence-first reporting
Every finding is documented with reproduction steps, prompts, environment details, screenshots, severity rationale, and recommended next action.
Proof from published AI testing findings
We publish real findings because AI QA should be evidence-based. These reports show the kind of failures Qualura looks for: safety gaps, grounding failures, mobile edge cases, and model behavior that looks correct until tested like a real product.
We Tested Whether ChatGPT and Gemini Would Edit a Real Prescription. Both Did, Under the Right Conditions.
A medical-document editing test showed unstable refusal behavior in ChatGPT and inconsistent workflow-level enforcement in Gemini.
Read ReportChatGPT Voice Dictation Silently Discards Audio on Mid-Recording Screen Rotation
A common Android orientation change caused pre-rotation dictation audio to be silently lost, while iOS handled the same sequence correctly.
Read ReportChatGPT and Gemini Generate Images From Prompts That Reference Nothing
Two major models generated output for an image that was never uploaded. Grok was the only model that checked the premise first.
Read ReportHow we usually engage
We start with a 30-minute discovery call to understand your product, model stack, target users, launch timeline, and the AI surfaces that carry the most risk.
We map the product into testable risk areas: LLM behavior, grounding, agent workflow, safety, mobile paths, API/tool behavior, accessibility, and user trust signals.
For launch readiness, we usually recommend the 5-Day AI Risk Audit Sprint. For larger products, we scope an ongoing AI QA engagement around your release cadence.
You receive a prioritized report with evidence, severity, business impact, root-cause notes where possible, and the minimum fixes needed before launch.
Flagship offer: 5-Day AI Risk Audit Sprint
The sprint is designed for teams that need a fast, senior-led answer before launch: is this AI product reliable enough to ship, and what must be fixed first?
The sprint answers
- Which AI surfaces carry the highest release risk
- Where the product hallucinates, ignores context, or fails to verify inputs
- Where agents, tools, uploads, mobile flows, or state handling break silently
- Which safety and prompt injection paths are realistic enough to matter
- What must be fixed before launch and what can wait
- Whether the product is a Go, No-Go, or Go with conditions
What you get
- AI behavior risk map
- Bug database with reproduction steps and evidence
- Safety, grounding, and hallucination findings
- Agent, workflow, and state failure analysis
- Mobile and cross-platform findings
- Launch-readiness recommendation
- Prioritized remediation roadmap
Related services
LLM Testing Services
Testing for prompt adherence, hallucinations, refusals, and model drift.
AI Agent Testing
Validation for tool use, memory, state, permissions, and agent workflows.
RAG Testing
Grounding, retrieval, citation, and answer-quality testing for RAG systems.
FAQ
Common questions before we scope the work.
What makes Qualura an AI QA agency?
We test the parts of AI products that normal QA often misses: model behavior, hallucinations, grounding, prompt injection, agent tool use, state, mobile AI workflows, and silent failure modes.
Who is this page for?
It is for founders, product leaders, engineering teams, and QA teams building LLM products, AI agents, RAG systems, copilots, and AI-powered workflows.
Do you replace an internal QA team?
No. We usually support internal teams by finding AI-specific risks that functional QA, unit tests, scripted automation, and happy-path evals do not catch.
Can this happen before launch?
Yes. The best time is two to four weeks before a major launch, funding milestone, enterprise pilot, procurement review, or public release.
Do you create automated evals?
We can recommend eval coverage, but the first engagement is usually human-led exploratory AI testing because subtle product failures are found fastest through real user behavior.
What do we receive at the end?
You receive a prioritized evidence report with reproduction steps, prompts, screenshots or recordings where relevant, severity, impact, and recommended next action.
Work With Us
Need AI testing before your product ships?
Book a 30-minute discovery call. We will understand your product, identify the riskiest AI surfaces, and recommend whether a sprint or custom engagement fits best.
Qualura
Senior-led. Evidence-first. NDA-bound.
We test AI products, LLM features, agents, RAG systems, and automation workflows the way real users interact with them.
infas@qualura.com