TeleTalker is the phone; ElevenLabs is the voice and the brains. This guide shows how to design, configure, deploy, and refine the conversational AI agent that actually talks to your callers — listen, think, speak.
The caller speaks → speech becomes text → the agent decides what to say or do → the answer is spoken back in your chosen voice.
Every production voice agent needs to listen, think, and speak in real time. ElevenLabs unifies these so you design conversations instead of stitching separate speech-to-text, language-model, and text-to-speech vendors together.
Real-time speech recognition (STT) transcribes the caller quickly and accurately so the agent can respond without awkward lag.
Agent logic routes the transcript through instructions, tools, and RAG-backed knowledge to decide what to say or do.
Low-latency TTS generates natural conversational audio in a voice chosen for the agent's role, brand tone, use case, and language.
For custom workflows, unique logic, and full control over behavior and tools.
For a fast start — a personal assistant or business agent you configure instead of building from zero.
For a TeleTalker virtual receptionist, a business-agent template is usually the fastest route; go blank when your call flow is unusual.
The use case and industry shape the agent's persona, tone, vocabulary, and what it should prioritize. Write it in plain language: who it is, who it serves, what it must never do, and when it hands off to a human.
You are a [role] for [business or team]. Your goal is to help callers with [main tasks]. Use a [tone] style. Only answer using the approved knowledge base when accuracy matters. If asked, clearly confirm you are an AI assistant — never claim to be human. Escalate to a human when [handoff conditions].
Filter thousands of voices by language, accent, tone, and use case. A support agent may sound calm and informative; a coaching agent may sound warm and encouraging. Match the voice to your brand and the caller's expectations.
| Context | Voice character |
|---|---|
| Customer support | Calm, clear, patient |
| Sales / bookings | Warm, confident, upbeat |
| Professional services | Measured, trustworthy |
| Hospitality | Friendly, welcoming |
A useful agent needs both facts and actions. Knowledge grounds it; tools let it do real work.
Upload URLs, files, or text so answers come from approved information, which reduces hallucinations.
Add webhooks, client tools, or integrations so the agent acts beyond talk.
action: book_meeting when to call: caller asks to schedule required fields: name, phone, preferred_time, topic success: confirm the booking was captured fallback: route to human if details are missing
Name knowledge sources clearly, keep them current, and remove outdated ones when policies or prices change — including the corrected $0.05/min rate.
TeleTalker is the integration surface: it receives caller audio, sends it to the agent, gets a spoken reply, and can trigger actions. The flow mirrors the listen–think–speak loop over a real GSM call.
| Area | What to prepare |
|---|---|
| Authentication | Use scoped API keys limited to only the endpoints the agent needs. |
| Streaming | Plan low-latency audio buffering so conversations feel natural. |
| Safety | Set credit quotas and monitor usage to avoid surprise costs. |
| Actions | Connect only the tools the agent needs, and test each with realistic calls. |
After launch, track whether the agent is useful, affordable, and responsive — then use real conversations to refine it.
Review metrics weekly during early launch, then settle into a steady cadence once the agent is stable. Configure a human-handoff path for repeated misunderstandings or failed tool actions.
A natural voice is a responsibility. Treat privacy and consent as part of the build, not an afterthought.
"Hi, this call is handled by an AI assistant and may be recorded for service quality. How can I help you?"