Frequently asked questions

A QA engineer executes within an existing system — writing test cases, running test suites, and reporting bugs. A QA consultant designs the system itself — the strategy, process, tooling, and metrics that determine how quality is managed across the entire engineering organization. QA consulting is appropriate when you need to build or rebuild your quality infrastructure, not just add capacity to it. After a consulting engagement, your existing team should be able to operate the improved system independently.

Your product likely needs a QA audit if: bugs are regularly reaching production despite testing, your test suite takes more than 30 minutes to run, your engineers are afraid to touch certain parts of the codebase, you don't know what percentage of your product is covered by automated tests, or you're preparing for enterprise sales and need to demonstrate software quality to prospective customers. A QA audit gives you a clear picture of your current state and a prioritized roadmap for improvement — without the overhead of hiring a full-time QA lead.

A standard QA Foundation Audit at QualityArk takes 2 weeks from kickoff to final report delivery. This includes a review of your current test coverage, CI/CD integration, bug tracking process, and team workflows, followed by a written report with prioritized findings and a 90-day improvement roadmap. Preliminary findings can be delivered at the midpoint of the engagement so your team can begin acting on the most critical issues immediately.

Yes. QualityArk works with B2B SaaS companies across Europe, including the UK, Germany, the Netherlands, Sweden, and other European markets. All client work is conducted in English. For remote-first engagements, we work within European time zones and communicate primarily via Slack and video calls. On-site work is available for clients in Poland and neighboring countries.

QA consulting costs vary depending on the scope and engagement model. One-time audits typically range from €1,500 to €5,000. Test automation sprints range from €3,000 to €10,000. Monthly retainer arrangements for ongoing QA support typically start at €2,000–€3,500 per month. Specialized services such as AI/LLM testing or security QA are priced based on the complexity of the system being tested. At QualityArk, all engagements are fixed-price — you know the cost before work begins, with no hourly billing surprises.

The free strategy call is a genuine conversation — not a sales pitch. We ask about your product, your engineering team, your current QA situation, and your biggest quality pain points. Based on that, we tell you honestly whether we can help and what we'd recommend as the right starting point. If there's a good fit, we send you a written scope and price within a few days. If not, we'll tell you that too, and suggest alternatives if we know of any.

We don't offer hourly billing. All QualityArk engagements are fixed-price packages with defined scope, deliverables, and timelines. We've found that hourly billing creates bad incentives — it rewards slow work and discourages efficient problem-solving. Fixed-price engagements align our interests with yours: we're motivated to deliver results as efficiently as possible. If you need a smaller, lower-commitment starting point, the QA Foundation Audit is designed exactly for that.

LLM testing is the process of systematically evaluating the behavior, accuracy, and safety of applications built on large language models. Traditional software testing cannot catch AI-specific failures: hallucinations, output inconsistency, prompt injection vulnerabilities, or RAG retrieval errors. If your SaaS product includes AI-powered features — a chatbot, intelligent search, AI assistant, or autonomous agent — standard QA is insufficient. LLM testing establishes a measurable quality bar for your AI features and helps you catch critical failures before your customers experience them.

RAG (retrieval-augmented generation) testing evaluates the full pipeline of a system that combines a language model with an external knowledge base. Standard API testing checks whether an endpoint returns the correct response for a given input. RAG testing evaluates whether the system retrieves the right documents, assembles them correctly as context, and generates responses that are accurate, grounded in the retrieved content, and free of hallucinations. It requires specialized metrics (context precision, context recall, faithfulness) and evaluation datasets that don't exist in conventional software testing toolkits.

Yes. QualityArk's AI testing approach works with any commercially available foundation model, including GPT-4, Claude, Gemini, and open-source alternatives like Llama 3 and Mistral. Our testing focuses on the application layer — how your product constructs prompts, processes retrievals, handles outputs, and manages edge cases — rather than the underlying model itself. The same testing framework applies regardless of which LLM your product uses.

Testing AI bias and fairness in SaaS products involves systematically evaluating model outputs across different demographic groups, input variations, and edge cases to identify patterns of differential treatment. We build evaluation datasets that represent the diversity of your user base, run the model against these datasets, and measure output consistency and quality across different signals. For products subject to the EU AI Act or operating in regulated sectors (HR tech, fintech, healthcare), we document findings in a format that supports regulatory compliance and can be shared with auditors or enterprise procurement teams.

Not necessarily. For most AI testing engagements, we work at the application layer — evaluating inputs and outputs — rather than requiring access to underlying model weights or proprietary training data. In most cases, black-box or gray-box testing is sufficient to identify the most critical quality and security issues. If deeper access is needed and available, we can also perform white-box testing of your prompt templates, retrieval logic, and agent orchestration code.