Text, image, and voice notes are all supported without breaking the customer experience or the operator workflow.
Case Study
AI-powered WhatsApp sales automation for text, image, and voice.
This build shows how a WhatsApp-first sales system can feel conversational for customers while still protecting pricing, product lookup, and operator visibility behind the scenes.
WhatsApp sales automation
multilingual support
voice and image input
PostgreSQL memory
The workflow routes text, image, and voice down the right path automatically so the team does not need a separate response process for every input type.
Why this system stands out
Conversation context lives in PostgreSQL so repeat customers do not restart from zero every time they return.
The system is better suited to Bangla, Banglish, and typo-heavy conversations than a generic template chatbot.
Workflow design highlights
The value of this build is not just the chatbot surface. It is the way the workflow keeps context, chooses the right tool path, and protects operator clarity.
- Automatic routing for text, image, and voice note inputs
- Vision and Whisper used for product lookup and transcription support
- Persistent memory in PostgreSQL for repeat-customer continuity
- Verified links and fixed pricing logic to reduce risky replies
- Feels closer to a strong human operator than a one-shot demo bot
- Reduces manual handoffs while still keeping the team in control
- Strengthens trust for mobile-first ecommerce and support conversations
Want a WhatsApp automation system that feels closer to a real operator?
This kind of workflow works best when the offer, the catalog logic, and the escalation rules are mapped clearly first. If that is the kind of system you need, send me the use case and I will point you toward the right build shape.