Hands-free customer notes via voice for busy mobile retailers.
On-the-go retail entrepreneurs struggle to manage customers effectively due to non-mobile-friendly customer management software.
VendVoice turns spoken notes into structured customer data using speech-to-text, optimized for noisy environments. Retailers dictate interactions, loyalty updates, and follow-ups on-the-go. Mobile app transcribes, categorizes, and syncs to a clean dashboard.
On-the-go small retail business owners and entrepreneurs
Voice-first input with noise-canceling AI for market chaos.
supportive
Record and transcribe customer interactions
AI tags notes as sale/follow-up/complaint
Voice-link notes to existing customers
Full-text search across voice notes
Set voice-activated reminders
Voice-to-SMS campaigns
Gauge customer mood from voice
Export voice notes as links
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| voice_profile | text | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| name | text | No |
| phone | text | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| customer_id | uuid | Yes |
| transcript | text | No |
| category | text | Yes |
| timestamp | timestamp | No |
| audio_url | text | Yes |
Relationships:
/api/notes/voiceTranscribe and save voice note
/api/customers/:id/notesFetch customer notes
/api/search/notesSearch transcripts
/api/remindersCreate reminder from note
No AI categorize
1000 min/mo
Unlimited
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 40 | 4% | $40 | $480 |
| Month 6 | 250 | 12% | $900 | $10,800 |
Voice-powered CRM for retailers who can't stop moving.
Target food truck Twitter communities and LinkedIn retail groups with voice demo Reels. Offer free Pro to first 10 signups in vendor forums. Partner with one market organizer for shoutout.
Ecom support
No voice input, desktop focus
Voice-first for physical retail mobility
Transcription
Generic, no CRM structure
Retail-specific customer linking
Improving voice models with user data for retail jargon accuracy.
Speech AI advancements (Whisper) + mobile mic hardware improvements.
Transcription accuracy in noise
Fallback to manual edit + noise filters
AI costs overrun
Cap calls, optimize prompts
Success: 80% prefer voice over type
Success: 70% accuracy self-reported
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