r/voiceagents

I’m exploring AI voice agents for outbound purchase calls and wanted to know how well they actually work.

Looking for insights on pickup rates, success/conversion rates, and how they compare to human agents. If you’ve built or used something like this, would love to hear your experience or any benchmarks.

Been running production voice AI agents for inbound calls using Vapi + n8n. Here's what I've learned after real deployments:

Stack:

- Vapi for voice (STT + TTS + LLM routing)

- n8n for orchestration (call flow logic, data routing)

- Webhooks into CRMs / Google Calendar / GHL

Key lessons:

1. Latency is everything — end-to-end response time above ~1.5s feels robotic to callers. Vapi's streaming helps but prompt engineering matters a lot.

2. Fallback handling is critical — if the agent can't answer something, it needs a graceful fallback (e.g., "I'll have someone call you back") rather than silence or loops.

3. Knowledge base quality determines call quality — garbage in, garbage out. The business-specific FAQ needs to be clean and well-structured.

4. Post-call summaries drive retention — business owners love getting a clean transcript + summary after every call. It builds trust in the system.

Current challenge: handling multi-turn conversations where the caller keeps changing their mind mid-booking.

What are others doing for state management in complex call flows? Any n8n or webhook patterns that work well?

After years of learning Ai I'm building a saas for voice agent which latency will be 400 to 500 way better than vapi or retell. ( 5 rupees per minute) Cause vapi and retell looks soo costly. As I'm building low as much as possible. If you spend on vapi or retell it would be around bring you 2k USD per month. As I'm building voice agent which comes under 400 to 500 USD. As per 10000 minutes per month.

Here my doubt is what's the latency most of the agencies cover up and how much we can claim to clients for voice agents? If we ping real estate people?

Sharing the architecture and lessons from building and deploying inbound voice agents for businesses. Happy to get into technical details with anyone building something similar.

Use case: Businesses that receive inbound calls but can't always have staff available. Agent handles the full call.

Stack:

- Vapi — voice layer, handles STT/TTS, manages call state

- n8n — orchestration, business logic, integrations

- Webhook triggers from Vapi into n8n on call events (started, ended, tool calls)

- Outputs: calendar booking, CRM updates, SMS/email confirmations, call transcripts to Notion/Sheets

Call flow:

1. Inbound call hits Vapi number

2. Assistant prompt + knowledge base loaded for the specific business

3. Tool calls trigger n8n workflows mid-conversation (e.g., check availability, book slot)

4. Post-call webhook sends full transcript + summary to business owner

Key learnings:

- Latency is the #1 UX factor. Keep tool call round trips under 1.5s or the conversation feels broken.

- Knowledge base structure matters more than prompt length. Short, factual KB entries outperform long narrative prompts.

- Always build an escalation path. Callers who get stuck or frustrated need a clean handoff to a human or voicemail.

- Test with real phone numbers early. Emulator testing misses a lot of real-world edge cases.

What telephony/orchestration stacks are others using for production inbound deployments?

I’m building a voice-based communication coach that talks to users in real time using the OpenAI Realtime API (POST https://api.openai.com/v1/realtime/sessions). The coach should act like a tough, high‑standards reviewer: very direct, candid, and focused on content quality first.

Even with a strict system prompt, the model keeps giving fake praise and calling vague answers “clear and easy to follow.”

Example (simplified):

• Coach prompt to user: “Give a 60-second status update to a senior stakeholder. Cover: (1) what was accomplished, (2) the biggest risk ahead, (3) one thing you need from them.”
• User answer: “We’re just working through the usual items.”
• Model response: “Your main strength is that your explanation was clear and easy to follow… For delivery improvement, try adding a slight pause… Keep going—you’re doing great!”
• What I actually want instead: Something like: “This is very vague. You didn’t say what was accomplished, what the biggest risk is, or what you need. This is not strong enough for a senior-level update. Try again, more specific but still high-level.”

My system prompt already includes things like:

• Be strict and candid; don’t sugarcoat.
• Only coach delivery when content is clear and specific.
• Give strong feedback on vague answers like “We’re just working through the usual items.”
• Don’t use phrases like “Great work”, “Your main strength is…”, “You’re doing great” unless the content is genuinely strong.
• If the answer is vague or incomplete, give 0% praise and 100% content-focused critique.

But the model still:

• Invents “strengths” for bad answers.
• Coaches delivery even when content is weak.
• Uses praise phrases I tried to ban.

I’m looking for:

• Concrete prompt patterns that actually reduce this “terminal niceness.”
• Ways (in a Realtime API / streaming setup) to force a content quality check and branch behavior.
• Examples of prompts or few-shot examples that produce a blunt, critical coach.
• Whether I should use a different model, add tool-calling / intermediate scoring, or post-process the streamed output to strip praise / reframe it.

If you’ve built strict/critical review or coaching agents (especially with the Realtime API), how did you stop them from reflexively saying “great job” and get them to honestly call out vague, low-effort answers?

Hey,

I’m building a voice agent with Vapi using German + Swiss German, and running into a few issues:

• Audio works fine
• STT misses simple words (even “hello”)
• Dialects/accents make it worse
• Sometimes the agent doesn’t respond at all
• There’s also noticeable delay

Feels like either model choice / language config / VAD is off.

Questions:

• Best STT model for German + Swiss German?
• Better to force de-DE or use auto-detect?
• Any tips for handling dialects reliably?
• How do you reduce latency in these setups?

Would love to hear what worked for others 🙏

Trying to see if any of yall are able to get real time lip sync working fluidly with an alternate voice map than native Azure/11-labs on d-id call ?