Direct Answer (TL;DR)
How does performance scale with high call volume? Brilo AI’s inbound-call AI voice agents scale based on peak concurrency, processing latency, throughput, and external integration capacity. Comprehensive AI solutions for call centers can support large numbers of simultaneous callers when account provisioning, telephony trunking, and integration endpoints are sized appropriately. Perform progressive load tests and share peak concurrency and latency metrics with Brilo AI Support to request production capacity increases or provisioning.
Why This Question Comes Up
Contact-center owners, platform admins, and SREs ask about scaling because unpredictable campaigns, product launches, and geographic expansion create sudden increases in incoming calls. Each AI voice agent session consumes compute, automatic speech recognition and synthesis, and integration calls. Planning for high call volume prevents degraded caller experience, higher error rates, and dropped sessions.
How It Works (High-Level — concurrency, latency, throughput)
Performance depends on a few measurable factors:
The number of simultaneous active calls (called concurrency). Estimate peak concurrent calls using: peak concurrent ≈ peak calls per second × average call duration (seconds).
Time to process audio and respond (called latency). Latency includes ASR (automatic speech recognition) processing, NLU (natural language understanding), response generation, and TTS (text-to-speech) synthesis.
Calls processed over time (called throughput). Throughput reflects sustainable requests per second or calls per minute.
Modern AI solutions for call centers maintain session context (conversation history) for accurate responses. Larger conversation history (model context) increases processing cost and latency. External integrations such as CRM writes or webhook calls add blocking latency unless those integrations are made asynchronous.
Guardrails & Boundaries
Brilo AI enforces operational guardrails to preserve quality under load:
Maximum allowed call duration and idle timeout to free concurrency quickly.
Confidence thresholds that trigger handoff (transfer to a human agent) when the AI voice agent confidence is low.
Limits on model context length to avoid unbounded latency growth.
Account provisioning caps and telephony carrier or SIP trunk limits; these require coordination with Brilo AI Support and telephony providers.
Safe behavior rules that prevent the AI voice agent from attempting regulated actions beyond approved workflows.
Applied Examples
Marketing campaign: Estimate peak calls/minute, calculate peak concurrent using average call length, then run staging tests at 50–120% of that peak.
Multilingual rollout: Include ASR and TTS tests for each language; additional languages increase ASR/TTS processing cost.
After-hours overflow: Configure call center AI solution to short-circuit simple intents and handoffs complex cases when concurrency crosses a threshold.
Human Handoff & Escalation
Handoff refers to transferring the caller to a live agent or external queue (called handoff). Fallback refers to simplified AI flows or retries before escalation (called fallback). Best practices:
Pass caller context and intent to the human agent at handoff to avoid repetition.
Define retry and elapsed-time thresholds that trigger automatic handoff during peak load.
Coordinate queueing strategy with telephony provider and CRM so the handoff path does not become a new bottleneck.
Setup Requirements
Before load testing or scaling production, provide:
Admin access or a named admin contact for the Brilo AI account.
Peak calls per minute, average call duration, and expected peak concurrent callers.
A staging phone number and realistic test scripts or recordings.
Confirmation that integration endpoints (CRM, ticketing, webhooks) accept burst traffic or support asynchronous processing.
Monitoring dashboards for concurrent sessions, 95th-percentile latency, error rates, ASR/TTS failures, inbound call automation, and integration errors.
Business Outcomes
Proper planning produces:
A predictable performance envelope for AI voice agents with defined peak concurrency and acceptable latency bands.
Reduced abandoned calls and improved first-response times during peaks.
Fallback and handoff plans that maintain caller experience even when near capacity.
Ability to scale call handling without linear increases in staffing.
Next Step
Calculate peak concurrent calls and run progressive load tests in staging starting at 10% of expected peak and increasing by 20–30% increments. Collect requests/sec, average call duration, 95th-percentile latency, and error rates. If test results approach account limits to your call center's AI solutions, book a call with Brilo AI for capacity provisioning or guidance on configuration and telephony trunking.