Brilo AI agents are trained through initial configuration, knowledge base ingestion, and ongoing self‑learning from live call interactions. Training starts with your documents and call scenarios, then Brilo AI refines responses using call transcripts, intent labels, and speech analytics. Call recordings and transcripts are stored according to your account configuration and retention settings. Brilo AI can export transcripts and events to your CRM or webhook endpoint for downstream processing and long‑term storage when integrations are enabled.

How do you train Brilo AI agents and where is call data stored? — Brilo AI ingests documents and call examples, then improves over time using live interactions.

Do you train Brilo AI with my call recordings? — Yes; when enabled, Brilo AI uses recordings and transcripts as training signals, subject to your retention policy.

Where does Brilo AI keep call transcripts and recordings? — Brilo AI stores them in your configured account storage and can forward copies to your CRM or webhook endpoint when you enable integrations.

Enterprise buyers need clarity on two things: how Brilo AI learns and what happens to sensitive call data. Call center teams want predictable onboarding (knowledge import, scenario setup, and test calls) and legal teams want clear data flows (where recordings live, who can access them, and how long they are kept). Procurement and security teams ask about exportability to internal systems and whether data can be scoped or deleted.

Brilo AI training follows three phases: seed, validate, and continuous learning.

During seed, you provide source material (scripts, FAQs, CRM fields) and define call scenarios and routing rules. During validate, Brilo AI runs test calls and referee scenarios to surface gaps in intents or knowledge. During continuous learning, Brilo AI uses anonymized call transcripts, intent tagging, and user feedback to refine responses and update the knowledge base.

A training corpus is the set of documents, sample calls, and tagged intents you provide for initial agent behavior. A knowledge base is the structured set of Q&A, policies, and script blocks the voice agent consults during calls. Brilo AI’s self‑learning behavior is described on the Brilo AI self‑learning voice agents page: Brilo AI self‑learning voice agents.

Technical terms used: training, fine‑tuning, knowledge base, speech analytics, call transcript, intent recognition.

Brilo AI should not be relied on to make legal, medical, or underwriting decisions without a human in the loop. Administrators can set explicit escalation conditions and disable learning from selected calls or data sources. Brilo AI will only incorporate call data into training when your account settings permit recording ingestion and when retention rules allow it.

A retention policy is the configured rule that determines how long transcripts and recordings are kept before deletion. Brilo AI provides answer‑quality controls so you can opt out specific queues or phone numbers from being included in automated training.

Do not use the agent as the sole decision maker for regulated outcomes. Use Brilo AI for information retrieval, routing, and first‑touch automation and require human approval for final decisions.

Healthcare example: A hospital contact center seeds Brilo AI with appointment scheduling scripts and patient intake forms. Brilo AI answers routine scheduling calls and logs transcripts to a secure account storage; medically sensitive or high‑risk calls are routed to a clinician for validation.

Banking example: A retail bank configures Brilo AI with product FAQs and account verification flows. Brilo AI fields balance inquiries and routes suspected fraud or loan‑decision requests to a specialist. Transcripts used for model improvement are exported to the bank’s CRM for audit and compliance review.

Insurance example: An insurer uses Brilo AI to intake claims notifications. Low‑complexity claims are prepopulated from agent transcripts; complex claims trigger an immediate human handoff and the call recording is flagged to be excluded from automatic retraining.

(These examples describe workflow patterns and what Brilo AI can be configured to do; they are not legal or compliance advice.)

Brilo AI supports several handoff patterns: warm transfer to an agent, creation of a ticket in your CRM, or routing to a specialized workflow. You can configure escalation triggers such as intent confidence below a threshold, mention of a risk term, or caller request for a human. When a handoff occurs, Brilo AI can pass context (dialog history, extracted entities, and suggested dispositions) so the receiving human has immediate context.

Handoffs can occur via SIP transfer, native transfer flows, or by creating an inbound work item in your CRM. For auditability, Brilo AI logs the handoff event and stores the associated transcript and metadata.

If you need a step‑by‑step implementation guide tailored to insurers or healthcare teams, see the Brilo AI insurance use case and product resources below.

When configured correctly, Brilo AI agents reduce time to answer for repetitive requests, increase first‑contact resolution for scripted inquiries, and provide consistent call summaries for faster downstream processing. Storing call transcripts and structured call events in your CRM supports audit trails, coaching, and faster claims or case resolution. These outcomes depend on proper setup, integration, and governance of training data and retention.

How long does Brilo AI keep call recordings and transcripts?

Retention is controlled by your account settings and retention policy. You choose retention windows and whether recordings or transcripts are exported to your CRM or webhook endpoint.

Can I stop Brilo AI from using certain calls for training?

Yes. Brilo AI allows you to exclude specific queues, phone numbers, or flagged conversations from the continuous training pipeline.

Can we export all transcripts to our internal storage?

Yes. Brilo AI can forward transcripts and structured events to your CRM or your webhook endpoint for internal archiving and compliance reviews when integrations are enabled.

Does Brilo AI redact sensitive information automatically?

Brilo AI supports metadata tagging and filtering to limit what is used for automated training; specific redaction features and PII handling should be confirmed with your Brilo AI account team and configured in your ingestion rules.

Will training change the agent’s live behavior immediately?

Changes from training cycles can be staged: you can test updates in a sandbox before promoting them to production, or enable continuous learning for incremental adjustments.

Next actions: schedule a technical kickoff with Brilo AI to review retention policy options, confirm export endpoints, and plan the seed training corpus.