Brilo AI uses a configurable Confidence Threshold to decide when an AI voice agent should stop trying to handle a call by itself and trigger clarification, retry logic, voicemail fallback, or a human handoff. The Confidence Threshold is evaluated from signals such as the intent confidence score, ASR (speech-to-text) confidence, audio quality metrics, and answer-quality checks; when combined signals fall below the threshold, Brilo AI follows the configured fallback rules. Administrators tune the Confidence Threshold per voice agent and per phone flow to balance automation coverage and safety. This behavior ensures predictable escalation for regulated or high-risk conversations.

Enterprises ask about Confidence Threshold because automated voice handling must be reliable and auditable in regulated sectors such as healthcare and financial services. Buyers need to understand when Brilo AI will continue an interaction versus when it will escalate to avoid incorrect transactions, privacy exposure, or regulatory mistakes. Clear rules around confidence reduce surprise transfers, lower false positives, and support compliance and quality-review workflows.

Brilo AI computes real-time quality and understanding signals for each utterance. Those signals include the ASR confidence for the latest utterance, the intent detection confidence from the NLU model, call audio quality indicators, and answer-quality heuristics. A decision layer compares a weighted combination of these signals to the configured Confidence Threshold and applies the first matching fallback rule (for example: retry, confirm, record voicemail, or escalate). Administrators can tune thresholds per phone flow so critical flows use stricter thresholds.

In Brilo AI, Confidence Threshold is the numeric cutoff that determines whether the agent continues automated handling or triggers fallback actions.

In Brilo AI, intent confidence score is the NLU model’s likelihood that the detected intent matches the caller’s request.

In Brilo AI, fallback rule is an action (clarify, retry, voicemail, or human handoff) that runs when confidence falls below the threshold.

For guidance on long conversations and when to tighten thresholds, see the Brilo AI article about long conversations and session limits: Can the AI handle long conversations?

Brilo AI enforces guardrails so low-confidence behavior does not cause harm. Typical guardrails include: disallowing the agent from taking high-risk actions when confidence is below a stricter threshold, requiring explicit user confirmation for transactional steps when ASR confidence is low, and invoking immediate human handoff for regulated topics. Brilo AI also logs the confidence signals and transcripts to support audits and quality reviews.

In Brilo AI, low-confidence handoff is a configured escalation that triggers when combined signals fall below the Confidence Threshold and other retry attempts have been exhausted.

For examples of guardrails around call quality and fallback behavior, see the Brilo AI guidance on handling poor call quality: Can the AI handle poor call quality?

When Brilo AI decides it is not confident enough, it follows your configured escalation workflow. Common options are: prompt the caller to try again, confirm details before proceeding, schedule a callback, capture a voicemail with structured metadata, or perform a warm transfer to a live agent. During any human handoff, Brilo AI includes the recent transcript, detected intent, confidence signals, and any collected form fields so the agent picks up with minimal repetition. You can configure warm transfer rules, callback behavior, and the amount of context passed to the human agent in the agent’s escalation settings.

See the Brilo AI setup notes about naturalness and agent deployment for practical configuration steps: Does the AI sound natural or robotic?

Configuring Confidence Thresholds in Brilo AI reduces the risk of incorrect automated actions, improves caller trust, and concentrates human attention on genuinely complex or sensitive calls. Proper thresholds lower agent rework by avoiding avoidable transfers while ensuring high-risk interactions receive human oversight. Auditable logs and transcripts support continuous model tuning and quality assurance.

What signals does Brilo AI use to compute confidence?

Brilo AI uses ASR confidence, intent/NLU confidence, audio quality metrics, and answer-quality heuristics. These signals are weighted by the decision layer to produce an overall confidence evaluation for each exchange.

Can I set different thresholds per phone flow?

Yes. Brilo AI supports per-flow and per-agent threshold configuration so high-risk flows (for example, financial transactions) can require stricter confidence before proceeding.

What happens to calls that are escalated due to low confidence?

Escalated calls follow your configured fallback: clarification prompts, retries, voicemail capture, callback scheduling, warm transfer to an agent, or routing to a specialist queue. The agent receives context and recent transcripts to reduce repeat questions.

How do we audit low-confidence decisions?

Brilo AI stores transcripts, confidence scores, and action logs for each call so your team can review patterns, retrain models, and adjust thresholds. These logs are accessible through the admin console and configured export endpoints.

Is there a limit to how many retries Brilo AI will attempt?

Retry limits are configurable per flow. Brilo AI recommends a small, predictable number of retries to avoid caller frustration and to ensure timely escalation when uncertainty persists.