Wavelength: AI Voice Agent Platform
Built a full voice AI pipeline from the transport layer up. 22,000+ calls across 7 businesses in 35 days. Not an API wrapper — real telephony, real conversations, real sales outcomes.
Calls made in 35 days
Autonomous AI conversations
More throughput than manual telecallers
Using the platform
See It In Action
A real call from production. Hit play to watch the AI qualify a lead in under 5 minutes.
AI Summary
The bot successfully welcomed Sachin, built excitement by discussing AI's relevance to his leadership role in oil and gas, and secured his verbal commitment to attend the masterclass this Saturday at 7:30 PM IST. He also confirmed receiving the joining links and agreed to join the WhatsApp group.
Extracted Data
Call Metrics
Conversation Flow
Watch the full Wavelength platform walkthrough:
The Problem
Our own company (Freedom With AI) runs weekly webinars for a community of 480,000+ learners. We needed to qualify leads, recover no-shows, and warm up registrants before each session. Doing this manually with a team of telecallers was expensive, inconsistent, and couldn't scale. We needed AI agents that could have real phone conversations — not chatbots, actual voice calls over telephony.
The existing off-the-shelf voice AI platforms were either too expensive at scale or didn't give us the control we needed over conversation design, voice quality, and call flow logic. At our volume, the per-minute costs from competitors would have been significantly more expensive than what we ended up building.
So we built our own.
What We Built
A full voice AI pipeline called Wavelength, built from the ground up — handling everything from SIP trunking to real-time LLM inference to text-to-speech synthesis:
- Custom voice AI orchestration layer managing the full call lifecycle from dial to hangup
- Enterprise telephony with SIP trunking, call recording, voicemail detection, and IVR/hold detection
- Multi-language speech-to-text optimized for 12 Indian languages — Hindi, English, Tamil, Malayalam, Telugu, Kannada, Bengali, Marathi, Gujarati, Punjabi, Odia, and Assamese
- Real-time LLM inference chosen for sub-second response latency in live conversation
- 24+ bot personas across 7 businesses — each with custom voice, personality, language, and conversation logic
- Smart safety systems — voicemail auto-detection (706 auto-hangups), IVR detection (338), DNC auto-enrollment (23 contacts), echo gate, ambient office noise injection, and 3-strike silence escalation in native language
We didn't use a no-code voice AI builder. We built the pipeline from the transport layer up, including writing a custom stateful overlap-save resampler to fix audio chunking discontinuities that were causing garbled speech at chunk boundaries. This is low-level DSP work, not prompt engineering.
System Architecture
Campaign Created
Leads Queued
Safety Gates (DNC / DND / Dedup)
Skipped
Circuit Breaker
Queue Paused
Outbound Dial
Voicemail / IVR Detection
Auto-Categorize
AI Conversation
AI Extraction
Lead Scoring
Hot → WA + Email + PDF + CRM
Warm → WA + CRM Tag
Smart Retry
DNC List
The Numbers — 35 Days in Production
Platform-Wide Performance
| Metric | Value | Context |
|---|---|---|
| Unique contacts called | 15,148 | Across 7 businesses in 35 days |
| Meaningful conversations held | 11,654 | 76.9% of contacts had a real conversation |
| Total talk time | 255.8 hours | Fully autonomous AI conversations — zero human intervention |
| Average call duration | 2 min 11 sec | Connected calls averaged substantive conversations |
| Longest conversation | 17 min 57 sec | AI held an 18-minute meaningful call without human intervention |
| Peak day volume | 4,399 calls | Single VM, zero downtime, zero degradation |
| Qualified leads (hot + warm) | 54.4% | More than half of all conversations yielded actionable leads |
| Positive + Neutral sentiment | 91.9% | Conversations felt natural — callers couldn't tell it was AI |
AI Call Impact on Webinar Attendance
We measured the direct impact of AI calls on webinar show-up rates across our own community of 480,000+ learners. The correlation was clear — more AI touchpoints, higher attendance:
| Metric | Value | Context |
|---|---|---|
| No AI call (baseline) | 30.9% show-up | Leads who received only email/WhatsApp reminders |
| 1 AI call completed | 40.5% show-up | +10% jump — single call moves the needle significantly |
| 2 AI calls completed | 52.4% show-up | +21.5% over baseline — optimal number of touchpoints |
Key finding: A single AI call delivers a 10 percentage point lift in show-up rate. Two calls deliver a 20 percentage point lift. This is the single highest-ROI intervention in our webinar funnel — and it costs ₹5.20 per minute (₹4.50 AI + ₹0.70 telephony).
Cost Comparison vs Manual Telecallers
| Metric | Value | Context |
|---|---|---|
| AI cost per minute | ₹5.20 ($0.062) | ₹4.50 AI calling + ₹0.70 telephony |
| Manual telecaller cost per minute | ₹5-15 | Similar per-minute cost, but limited to 100-200 calls/day |
| Total spend (255 hrs) | ~₹79,560 ($953) | For 22,000+ calls across 7 businesses |
| Throughput advantage | 22-44x | 4,399 calls/day on one VM vs 100-200 per human agent |
| Works 24/7 | Yes | Consistent quality, 10 languages, no sick days, no training |
| Cost per qualified lead | ~₹14.55 | ₹79,560 / 5,469 qualified leads — at scale, unbeatable |
Weekly Growth Trajectory
| Metric | Value | Context |
|---|---|---|
| Week 1 (launch) | 1,850 calls, 52s avg | System goes live — initial calibration |
| Week 2 | 2,940 calls, 68s avg | 1.6x volume, conversations getting longer |
| Week 3 | 4,210 calls, 85s avg | Prompt tuning kicks in — call quality improves |
| Week 4 | 5,875 calls, 105s avg | 3.2x launch week volume, 2x call duration |
| Week 5 (peak) | 9,526 calls, 120s avg | 5.1x launch week — platform at full scale |
| Week 6 (partial) | 4,303 calls, 130s avg | 2.5x longer conversations than week 1 — system getting smarter |
Campaign Types
Lead Qualification
The primary use case. AI calls registrants, qualifies interest level, and categorizes leads as hot, warm, or cold. Connected calls average 91 seconds — substantive conversations, not robocalls. 60.5% qualification rate on connected calls. For every 100 contacts reached, ~25 are qualified leads ready for follow-up.
Event Day Reminders
Quick, purpose-driven reminder calls on webinar day. 58.5% qualification rate. Average duration: 54 seconds — efficient and high-converting. This is where the +20% show-up rate lift comes from.
Voice Persona A/B Testing
Systematic testing of different voice personas, accents, conversational styles, and languages to optimize engagement. We can A/B test at scale — something impossible with human telecallers — and iterate on persona design in hours, not weeks.
Multi-Client, Multi-Language
Wavelength isn't a single-client tool. It serves 7+ businesses across 24+ bot personas in 12 Indian languages, all running on the same infrastructure.
Supported Languages
| Metric | Value | Context |
|---|---|---|
| Active businesses | 7+ | Education, coaching, wellness, music — diverse verticals |
| Bot personas deployed | 24+ | Each with custom voice, personality, language, and conversation logic |
| Avg connect rate (warm lists) | 70-82% | Curated, warmer audiences connect at significantly higher rates |
Key Engineering Challenges
1. Sub-Second Voice Latency: In a phone conversation, any delay over 800ms feels unnatural. We built a custom Voice Activity Detection pipeline with adaptive buffer windows that distinguish natural speech pauses from end-of-utterance — ensuring the AI responds at human-conversation speed without cutting off the caller.
2. Audio Chunking Discontinuities: TTS output arrives in chunks that don't align at sample boundaries, causing audible pops and garbled transitions. Built a stateful overlap-save resampler that maintains phase continuity across chunk boundaries. This is low-level DSP work, not prompt engineering.
3. Concurrency at Scale: Running hundreds of simultaneous AI conversations requires careful resource management. We built intelligent call staggering, connection pooling, and session isolation — allowing the system to handle peak loads without degradation. Data-driven tuning: we profiled call patterns by hour and optimized concurrency limits per time slot.
4. 4,399 Calls/Day on a Single VM: The entire platform runs on a single cloud VM (2 vCPU, 8GB RAM, ~$50/month). We pushed 4,399 calls through it in one day with zero downtime, zero degradation. Careful connection pooling, session management, and resource cleanup prevent memory leaks under sustained load.
5. Production-Grade Safety Systems: 679 voicemails auto-detected and hung up. 324 IVR/hold systems detected (bot recognizes “press 1 for...” and exits gracefully). Automatic DNC enrollment when someone says “stop calling.” Echo gate prevents the bot from hearing its own voice echoed back. Ambient office noise injected to sound more human. 3-strike silence escalation in native language before hanging up.
Want This for Your Business?
Wavelength is battle-tested across 7 businesses, 24+ bot personas, and 10 languages. We're selectively opening it up to businesses that want AI voice calling without building the infrastructure themselves.
If you're interested in using Wavelength for your business — or white-labeling it under your brand — reach out to us.
Visit Wavelength Platform →24+
Bot personas ready to deploy
10
Languages supported
22,000+
Calls proven in production
The Bottom Line
5,469
Qualified leads generated for one client in 35 days
255 hrs
Of autonomous AI conversations handled
22-44x
More throughput than manual telecallers
35 days
From first commit to 7 businesses live in production
Want to build something like this? Let's talk.