Quality evals
Our technology is engineered to deliver uncompromising reliability
Regression Eval
Before answering a single live ticket, we build regression datasets from conversations your agents have already resolved and use them for back-testing—catching errors or bias before they ever reach a customer.
ID
Message
Actual Output
173301
Good afternoon, I wanted…
APPOINTMENT_RESCH...
173302
I need to reschedule…
APPOINTMENT_RESCH...
173303
Is my appointment sched...
APPOINTMENT_RESCH...
173304
Hello, I need to change…
APPOINTMENT_RESCH...
173305
Can I move it to Monday…
APPOINTMENT_RESCH...
173306
I would like to confirm my…
APPOINTMENT_RESCH...
Eval model in production
We train an LLM-as-a-judge that mirrors expert evaluators’ criteria. It analyzes every AI response and scores dimensions like accuracy, compliance, tone, and resolution—all customized to your needs.
Adherence
Score
5
The agent consistently addresses user concerns about audiobooks and search, keeping responses relevant and including greetings when prompted.
Assertiveness
Score
5
The agent provides clear, confident guidance, avoiding uncertainty and offering actionable steps for using search.
Fluency
Score
5
Messages are fluent, error-free, easy to understand, and maintain a friendly, helpful tone throughout.
Groundedness
Score
5
Responses are based on documentation, providing clear instructions tied to detected intents (e.g., ‘how_to_find_contents’).
Evaluation
5
Continuous optimization
Once the system is running, tickets with poor CSAT or flagged by the evaluation model are automatically marked. Our prompt-engineering team reviews them, fixes issues, refines the AI, and adds new training data—creating a continuous-learning loop that makes the system better every day without disrupting your operation.
Security & privacy
All processing stays in the EU: Storage, inference, and even LLM compute run exclusively in European data-centres, so your data never leaves the jurisdiction of GDPR.
Conversation transcripts and customer records are kept only for short-lived evaluation cycles, encrypted at rest and in transit, and automatically purged when the evaluation window closes.