Test your chatbot before launch
Runs thousands of realistic synthetic users against your chatbot, scores every conversation and gives each release a readiness score.
Ship chatbots you can trust — before real users find the problems
Launching a chatbot without rigorous pre-release testing means your real users become your QA team — discovering failures in tone, accuracy, compliance, and edge-case handling at the worst possible moment. Allmaz eliminates that risk by generating thousands of realistic Azerbaijani synthetic users and running them against your chatbot before a single real conversation takes place. Every synthetic user is modelled on authentic Azerbaijani conversational patterns, covering the full range of intents, emotional states, and language variations your chatbot will encounter in production. The result is a test population broad enough to surface the failures that hand-written scripts routinely miss.
Why teams choose Allmaz for chatbot quality assurance
Catch failures before real users do by simulating thousands of realistic Azerbaijani conversations at scale, covering the breadth of intents and emotional states a live deployment will face
Pinpoint exactly where your chatbot breaks — whether the issue is factual accuracy, inappropriate tone, mismatched formality, or a compliance boundary violation — rather than receiving only a pass or fail signal
Stress-test adversarial edge cases including frustrated users, self-contradictory inputs, prompt-injection attempts, and natural Azerbaijani–Russian code-switching that standard QA scripts consistently overlook
Prevent regressions with automated suites that replay scenarios tied to previously identified issues on every new build, confirming fixes hold and new changes introduce no old problems
Align your entire team around a single release readiness score that translates detailed conversation-level results into a shared go/no-go language accessible to both engineers and stakeholders
Integrate without disruption by connecting Allmaz through REST, Dify, Kommunicate, or browser automation — whichever interface your current deployment already exposes
What Allmaz brings to chatbot quality assurance
Realistic synthetic user generation
Allmaz generates thousands of synthetic users modelled on realistic Azerbaijani conversational patterns, giving your chatbot a broad and representative test population before any real user interacts with it.
Adversarial scenario coverage
Test suites include frustration escalation, self-contradictory requests, prompt-injection attempts, and natural Azerbaijani–Russian code-switching — the edge cases that standard QA scripts routinely miss.
Azerbaijani-native LLM judge
Every conversation is scored by an LLM judge built for Azerbaijani language and context. It evaluates accuracy, tone, formality level, and compliance — not just whether the bot produced a response.
Release readiness score
Each build receives a single readiness score that aggregates all conversation-level results, giving your team a consistent, comparable signal across releases rather than a spreadsheet of raw logs.
Regression suites
Automated regression suites replay scenarios tied to previously identified issues, confirming that fixes hold and that new changes have not reintroduced old problems.
Flexible integration options
Connect Allmaz to your chatbot via REST API, Dify, Kommunicate, or browser automation, fitting into the deployment setup you already have without requiring a platform migration.
How chatbot testing works with Allmaz
Common questions about chatbot testing with Allmaz
Why do I need synthetic users instead of manual test scripts?
Manual scripts cover only the paths your team anticipates. Synthetic users simulate the breadth and unpredictability of a real user population — including edge cases, varied emotional states, and natural language variation — at a scale that manual testing cannot reach before a launch deadline. For a bilingual Azerbaijani market where conversational norms and code-switching patterns add further complexity, that coverage gap is especially costly to discover after go-live.
What does the Azerbaijani-native LLM judge actually evaluate?
The judge scores each conversation across four dimensions: accuracy (did the bot answer correctly), tone (was the response situationally appropriate), formality (did it match the expected register for the context), and compliance (did it stay within defined operational boundaries). All four dimensions are evaluated with direct awareness of Azerbaijani language norms and cultural context, rather than being inferred from a general-purpose model trained on other languages.
What is a release readiness score and how should my team use it?
The readiness score is a single aggregated value calculated from all conversation-level scores produced during a test run. It gives your team a consistent, comparable signal across builds — useful for setting internal quality thresholds, tracking improvement over successive releases, and communicating go/no-go status to stakeholders who should not need to review raw conversation logs to understand whether a build is ready.
How do regression suites prevent old problems from returning?
When an issue is identified and resolved, Allmaz records the specific scenarios that exposed it. On every subsequent test run, those scenarios are replayed automatically. If a new change reintroduces the problem, the regression suite flags it immediately rather than allowing it to reach production. This gives teams confidence that velocity on new features does not come at the cost of stability on previously fixed behaviour.
Does Allmaz work with the chatbot platform we already use?
Allmaz connects via REST API, Dify, Kommunicate, or browser automation. If your chatbot is accessible through any of these interfaces, integration requires no changes to your existing deployment setup. The goal is to fit Allmaz into the pipeline you already operate, not to require a platform migration as a prerequisite for better testing.
Ready to know your chatbot is ready?
Run your first synthetic test suite with Allmaz and get a release readiness score before your next launch. Contact the Allmaz team to get started.
Request a demo