Postgres to Telegram, instant answers without SQL
Ask questions in Telegram and get safe Postgres answers back. No raw access, no huge…
Deploy voice agents that book meetings, qualify leads, and answer FAQs—then log every call, route follow-ups, and keep your CRM and inbox synced automatically.
A typical setup starts with a call event (often via Twilio) that triggers an n8n workflow. The audio is transcribed, then an AI model like OpenAI turns it into a short summary, key fields (name, company, intent), and recommended next steps. From there, the workflow updates your systems: a row in Google Sheets, a message to Slack, and an email follow-up. You can also add rules to route urgent calls to the right person.
Not really. Most Flowpast workflows are plug-and-play: you connect your accounts, paste a few API keys, and adjust simple fields like “send summary to this Slack channel.” If you can use Google Sheets and follow a checklist, you’ll be fine. When you want custom call flows, branching logic, or CRM mapping, light technical help can speed things up, but it’s optional.
For many teams, it replaces about 2 hours a day of manual busywork: writing call notes, copying details into sheets, and sending follow-ups. The bigger win is consistency. Every caller gets a response, every deal has a summary, and nothing sits in someone’s memory. If you handle high volume, savings can jump quickly because the workflow runs the same way on every call.
You need an n8n workspace (cloud or self-hosted), a voice/calling provider like Twilio, and access to an AI model (OpenAI is common). Have a destination ready too—Google Sheets, Slack, or your CRM—so the workflow has somewhere to write results. You’ll also want a basic call script or intent list (sales, support, booking) so the agent knows what to collect. Start with one call type, then expand.
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