Hey everyone! 👋
I recently worked on a real-world problem: field workers and operators hate typing long reports on their phones, but sending random voice notes creates a massive mess for the data analysis team.
I designed a no-code/low-code architecture to solve this. The goal? Let users send a simple WhatsApp audio, and automatically transform that unstructured voice into a clean, structured JSON format ready for Google Sheets or PowerBI.
Here is the high-level architecture of the workflow I built:
🛠️ How the workflow actually works:
- 🟢 WhatsApp Cloud API Webhook: It listens for incoming messages. If it's a text, a Router sends it straight to the LLM. If it's an audio file, it moves to the next step.
- ⚠️ The Tricky Part (Media ID): WhatsApp webhooks don't send the actual audio file, just a
Media ID. You need an HTTP module to download the.oggfile using the WhatsApp Business API. - 🎙️ OpenAI Whisper: We pass the downloaded
.oggfile to Whisper to get a highly accurate text transcription. - 🧠 Chat Completion (JSON Extraction): We send the transcribed text to ChatGPT with a strict system prompt to extract specific entities (like quantities, product names, dates, or client IDs) and output them in a valid JSON format.
- 📊 Parse JSON & Export: Once parsed, the data is perfectly mapped to columns in Google Sheets or sent to a PowerBI dataset.
🚀 Why this matters?
This completely removes the friction of manual data entry. Operators just talk to WhatsApp, and the database updates in seconds with zero human typos.
💡 If you are building something similar and get stuck on the WhatsApp Media download step or the JSON parsing, let me know in the comments. I'd be happy to discuss the logic!
Have you implemented LLMs for structuring field data? How did you handle it?
🔗 Let's connect on https://www.linkedin.com/in/nickmed/ if you want to talk about process automation!
Top comments (0)