Capturing email addresses over voice AI systems like VAPI and Twilio remains one of the thorniest challenges in building reliable call flows. Even today, transcription errors, user behavior, and technical limitations can break otherwise good conversations — because with emails, *"almost right" is as bad as "wrong".
This guide distills research from real-world developers, official documentation, and user case studies into a practical, up-to-date framework you can actually apply.
Core Pain Points in Voice Email Capture
| Area | Details |
|---|---|
| Transcription Accuracy | Frequent errors come from background noise, unclear pronunciation, and especially letter-by-letter spelling — engines often split or misinterpret characters, causing invalid addresses. |
| Email Formatting Issues | Special symbols like "@" and "." often get misheard as "at" and "dot" text. Capitalization errors further complicate parsing. |
| Speech Patterns | Fast or unclear spelling worsens transcription. Accents, background noise, and nonstandard pauses cause confusion. |
| System Limitations | Even advanced systems like VAPI and Twilio aren't inherently optimized for structured entity capture like emails without special configuration). |
| User Frustrations | "Almost right" emails are useless. Developers call it one of the biggest pain points in deploying voice AI for business workflows. |
It has been one of our biggest struggles collecting email addresses during phone calls using voice AI… the email was most likely spelled wrongly and invalid. We see this every week." Source
Common Solutions and Workarounds
| Area | Details |
|---|---|
| Prompt Engineering | Asking users to "spell email slowly, letter by letter" dramatically improves results. Adding examples reduces confusion. |
| Spelling Modes | Some platforms offer spelling-focused modes like Voiceflow entity capture. VAPI supports entity extraction for emails if configured properly. |
| Validation After Capture | Always regex-validate captured emails immediately. Best practice adds an API verification like ZeroBounce for extra safety. |
| Retry and Confirm Loops | Always repeat captured email back for confirmation: "I heard john.doe@gmail.com. Is that correct?" |
| Alternative Channels | When accuracy drops, send SMS/email links for manual address input. Avoid frustrating users with endless retries. |
Example Prompt Template:
“Please spell your email address, letter by letter. For example, J as in John, A as in Apple…”
After capture: “I heard john.doe@gmail.com. Is that correct?”
Platform Differences: VAPI vs Twilio
| Feature | VAPI | Twilio |
|---|---|---|
| Transcription Engines | Choice of Deepgram, Whisper, Gemini. | Native Twilio transcription. |
| Entity Extraction | Native email entity parsing. | Requires manual regex and validation setup. |
| Latency | Faster (800ms–1.2s typical). | 1.5–2s typical. |
| Capture Accuracy | Up to 92% with optimized prompts. | ~85% under best conditions. |
Key Point: VAPI’s flexibility with transcription engines gives builders an advantage, but prompt engineering still matters more than the engine itself.
Best Practices for Email Capture in Voice AI
Always Prompt for Letter-by-Letter Spelling
- Teach users how to say their email before asking for it
Repeat Back and Confirm
- Capture the email, then immediately confirm
- If needed, retry politely
Regex Validate + API Verify
- Use basic regex:
^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$ - Then verify with a service like ZeroBounce for extra confidence
Offer SMS/Web Form Fallbacks
- After two failures, offer to send a link instead
Monitor and Adjust
- Log failure cases
- Use common mistakes to improve prompts and validations
AgentVoice Advantage: Smarter Email Capture Built In
Every voice AI system — even the most advanced — depends on transcribers to feed information to their language models. If the transcription is flawed, the agent can fail, no matter how smart the LLM is. At AgentVoice, we solve this at the root by pairing the most accurate transcription engines available with cutting-edge large language models trained to intelligently work around minor transcription errors, dramatically improving email capture reliability.
Inside AgentVoice, we’ve already tackled the core pitfalls of email capture:
- Pre-built prompts optimized for letter-by-letter spelling.
- Dynamic fallback flows if transcription looks invalid.
- Integration-ready outputs for CRMs, SMS confirmations, and lead tracking.
Built-in LLM Recovery for Misspelled Emails
AgentVoice leverages large language models that are trained to interpret common letter spelling errors caused by transcription, such as interpreting “Aye” as “A.” This allows the system to intelligently correct minor phonetic variations before confirming with users.
Optimized Readback for Clearer Confirmations
We’ve designed readback flows that force letter-by-letter spelling with slight pauses and periods (e.g., “S. L. A. V. I.”) to ensure text-to-speech engines pronounce each letter distinctly — avoiding user confusion even when audio quality is imperfect.
Live Email Confirmation Support
In addition to regex and API validation, AgentVoice optionally sends a real-time confirmation email to the captured address, giving users instant feedback that the address is correct and actionable.
Key Differentiator: While most voice platforms stop at transcription, AgentVoice uses layered validation, optimized readback, and real-world confirmation to minimize capture errors at every stage.
If you’re tired of losing good leads to bad transcription, try AgentVoice today.
Save time. Capture more. Close better.