Direct Answer (TL;DR)
Brilo AI Failure Analysis identifies why calls did not reach a successful resolution by combining call transcription, intent recognition, confidence scoring, and post-call conversation analytics. The feature flags unresolved intents, groups similar failures for root cause analysis, and surfaces recommended routing or script changes to reduce repeated failures. Brilo AI Failure Analysis can be configured to export failure events to your CRM or webhook for follow-up and to create human escalation tickets when confidence scores fall below a threshold. This lets operations teams prioritize fixes and track remediation over time.
How does Brilo AI detect failed resolutions? — Brilo AI flags unresolved intents and low confidence scores from call transcription and intent classification.
What counts as a failed call resolution? — Brilo AI treats calls as failed when configured resolution criteria (intent match, transfer success, or explicit closure) are not met.
Can Brilo AI notify teams about failed calls? — Yes. Brilo AI can generate failure events and send them to your CRM or webhook for human follow-up.
Why This Question Comes Up (problem context)
Enterprises need to know not just that a call failed, but why it failed and how often the same failure recurs. Healthcare, banking, and insurance teams face regulatory and operational risk when issues go unresolved. Buyers ask about Failure Analysis to understand whether Brilo AI will reliably detect unresolved calls, surface root causes, and feed corrective workflows without creating noise for human teams.
Typical concerns include: distinguishing true failures from edge cases, preventing repeat mistakes in automated scripts, and ensuring sensitive calls are escalated properly for compliance and customer experience reasons.
How It Works (High-Level)
Brilo AI Failure Analysis evaluates each completed call against configured resolution criteria and automated analytics:
Call transcription is used to extract utterances and recognized intents.
Intent recognition and a confidence score determine whether the system achieved a match.
Post-call conversation analytics group failures by intent, phrase, or routing step to support root cause analysis (RCA).
Failure events are tagged and stored for reporting, trend analysis, and manual review.
In Brilo AI, failure analysis is the process of identifying and grouping unresolved calls for diagnostic review. A failed call resolution is a call that does not meet the configured success conditions (intent match, confirmation, or successful handoff). A failure tag is an automated label applied to a call recording or transcript indicating the failure type.
(Technical terms used: call transcription, intent recognition, confidence score, root cause analysis, failure tagging, conversation analytics.)
Guardrails & Boundaries
Brilo AI Failure Analysis is designed to reduce false positives and avoid inappropriate escalation:
Brilo AI will not automatically treat every low-confidence utterance as a failure; you configure thresholds for confidence scoring and resolution criteria.
Brilo AI will not perform automated remediation that changes customer data without explicit workflow permissions and configured integrations.
Brilo AI will not provide legal or medical advice; flagged calls containing sensitive health or financial information are routed to human review when configured.
In Brilo AI, an escalation condition is a configured rule that routes a flagged failure to a human when certain criteria (confidence score, keywords, or explicit caller requests) are met.
Applied Examples
Healthcare example:
A patient calls to update care instructions but the Brilo AI voice agent misclassifies the request. Failure Analysis groups similar transcripts showing missed phrases like “medication schedule,” surfaces the affected script node, and creates an escalation ticket for a nurse when protected health information (PHI) keywords appear.
Banking example:
A customer attempts to dispute a transaction. Brilo AI transcription records the interaction but intent recognition yields low confidence. Failure Analysis tags the call as “dispute_intent_low_confidence,” shows a spike in that tag for a given script version, and recommends routing those calls to a trained fraud-resolution specialist.
Insurance example:
Callers trying to file a claim repeatedly get an automated denial because of a missing confirmation step in the AI script. Failure Analysis identifies the workflow step where callers drop off and recommends inserting an explicit confirmation prompt or a warm handoff to a claim agent.
Note: Do not interpret these examples as legal or compliance advice. Use your policies and Brilo AI configuration to meet regulatory requirements.
Human Handoff & Escalation
Brilo AI supports multiple handoff patterns when a failure is detected:
Conditional handoff: when the confidence score is below a configured threshold, Brilo AI can route the caller to a human agent or open a callback ticket.
Immediate escalation: when explicit keywords or phrases that indicate risk are detected, Brilo AI creates a high-priority escalation to a designated team.
Deferred follow-up: Brilo AI can add failure events to a queue for asynchronous human review, combining the transcript, summary, and failure tag.
Workflows are controlled in Brilo AI by rules you set: set thresholds for confidence scoring, define keyword-based escalation lists, and map failure tags to specific routing destinations or ticket types.
Setup Requirements
Define: Establish what “resolved” means for each use case (intent confirmation, transfer completed, explicit caller confirmation).
Provide: Supply sample calls and transcripts to improve your intent models and failure tagging.
Configure: Set confidence score thresholds and specify escalation rules for low-confidence or risky calls.
Connect: Integrate Brilo AI with your CRM, ticketing system, or webhook endpoint to export failure events and create follow-up tasks.
Test: Run pilot calls to validate failure detection, handoff behavior, and the volume of created tickets.
Monitor: Establish dashboards and weekly reviews to tune intent models and routing rules.
(Required inputs typically include example transcripts, resolution criteria, and the address of your webhook or CRM endpoint. Brilo AI will use these to classify failures and notify downstream systems.)
Business Outcomes
Faster identification of systemic issues in automated scripts and call routing, enabling targeted fixes.
Reduced repeat failures by using grouped failure tags to prioritize development or script updates.
Lower operational risk through configurable escalation rules that ensure sensitive or high-risk unresolved calls reach humans.
Better continuous improvement: Failure Analysis provides the data needed to iterate on intent models and operator training.
These outcomes focus on operational clarity and reduced risk rather than guaranteed performance metrics.
FAQs
How does Brilo AI define a failed call?
A failed call in Brilo AI is any call that does not meet your configured resolution criteria—this may be an unmatched intent, missing confirmation, or an unsuccessful handoff. You set those success conditions during configuration.
Can Brilo AI detect repeated failures across time?
Yes. Brilo AI groups failure events by tag, intent, or script node so you can identify trends and recurring root causes over days or weeks.
Will Brilo AI automatically retry a failed call?
Brilo AI does not automatically retry calls unless you configure a retry workflow. You can create rules that schedule callbacks or route callers to alternative human teams after a failure.
Can Failure Analysis expose PHI or sensitive data?
Brilo AI includes configurable guards to escalate calls containing sensitive keywords to humans. You should configure filtering and retention policies per your compliance requirements; Failure Analysis itself does not replace your compliance controls.
How are failure confidence scores calculated?
Confidence scores come from Brilo AI’s intent classifier and transcription accuracy. You can adjust the threshold that separates acceptable confidence from a flagged failure.
Next Step
Contact your Brilo AI account team to request a Failure Analysis configuration review and pilot setup.
Provide example call transcripts and your resolution criteria to Brilo AI support so they can help tune intent models and confidence thresholds.
Schedule an operational review with Brilo AI to define escalation rules, webhook destinations, and reporting cadence for ongoing failure monitoring.