AI Quality Engineering · Voice · Chat · Agents
I test voicebots, chatbots and AI agents. I catch regressions, broken business flows and response-quality issues before your users do.
Test case · demo
RESCHEDULE_APPOINTMENT
Move my appointment to Thursday at 4:00 PM.
Done. Your appointment has been moved to Thursday at 4:00 PM.
RESPONSE QUALITY · PASS language · intent
Final result
FAIL · REGRESSION DETECTED
The bot confirmed an operation that the backend did not execute.
What I test
An LLM can produce a perfectly reasonable response while using the wrong data, losing context or failing to execute the required action. That is why I test the full process, not only the final response.
01 · Tool call
BOT
“Reservation cancelled.”
✕ FAIL · FALSE CONFIRMATION
02 · RAG
USER
“How long do I have to return this item?”
✕ FAIL · WRONG RETRIEVAL
03 · Multi-turn
USER
“Actually, not 2:00 PM. Make it 4:00 PM.”
✕ FAIL · STALE CONTEXT
Path of a single request
I choose test layers based on the system architecture and process risk. A final PASS requires not only a correct response, but correct system behaviour as well.
Services
Do you build AI systems for clients?
PRIMARY SERVICERepeatable tests after changes to prompts, models, RAG, integrations or code. They show what improved and what silently stopped working.
Also available white-label as an additional QA layer before client handover.
AI Regression Suite: scenarios · golden dataset · evaluators · regression reports · optional CI
Do you already use an AI system?
I independently verify whether the delivered system actually performs the agreed business processes, uses the correct data and behaves properly outside the happy path.
acceptance report · critical blockers · evidence · GO / FIX / NO-GO
Need a point-in-time diagnosis first? Report, evidence, severity and recommendations for the system’s current behaviour.
Need ongoing quality monitoring after deployment? Recurring regression runs, version comparisons and quality trends.
Starting point
5 targeted scenarios for one critical process.
After you submit the form, I will review the system description and select five scenarios to test. If I need additional context or access to a test environment, I will contact you by email.
no sales call · no commitment · about 3 minutes to complete the form
Sample report
Every finding includes the scenario, preconditions, expected result, actual result, severity and evidence of what went wrong.
Sample report built on a fictional system
36 scenarios · 128 executions
SCENARIO PASS RATE
78%
28 / 36 scenarios
28 PASS
8 FAIL
RELEASE RECOMMENDATION · NOT READY
The original appointment is cancelled before the new slot is confirmed. The patient ends up with no appointment while the agent confirms success.
Demo data. None of it comes from a client.
Who runs the tests
AI Quality Engineer
I work hands-on with production voicebot and chatbot evaluation. Before moving into AI quality, I spent more than three years in QA for energy-market systems, where software failures had real business consequences.
I design tests around business rules, multi-turn conversations, RAG, integrations and the final system state.
LinkedInFAQ
No. A large part of the testing can be done black-box through the interface, phone number or API. Log access is only needed when we want to evaluate a specific stage of the process, such as the retriever or tool calls.
No. I care about the full process: the response, context, business rules, tool call, API and final system state. A bot can produce a perfect-sounding answer and still get a FAIL.
Five targeted scenarios for one critical process: the core flow, a business rule, a multi-turn correction, a tool call or grounding case, and failure recovery. The result is a short PASS / FAIL report with concrete observations. No sales call.
Choose one key process. I will design five targeted scenarios and show where the system behaves correctly and where the risk is.