Case fileAI Systems & Automation
An end-to-end sales funnel for a crypto-mining hardware enterprise: Instagram and WhatsApp leads qualified, answered from a catalog of 500+ ASIC miners, scored, and handed to sales.
A crypto-mining hardware enterprise received sales enquiries through Instagram direct messages. Each one had to be answered with accurate product knowledge across a catalog of more than 500 ASIC miners, qualified, and passed to a salesperson when it was worth their time.
Prospects did not write tidy text. They sent voice notes and photographs of hardware alongside messages, and the conversation moved from Instagram to WhatsApp before a sale. The requirement was to handle those conversations without a person reading every message, while keeping product answers accurate and getting real buyers to sales.
What made it non-trivial
Qzentra built the complete funnel as one production workflow, orchestrated in n8n. Instagram direct messages enter the system and are qualified; the conversation continues on WhatsApp, where a multimodal AI layer handles text, voice notes, and images.
Product questions are answered by retrieval over an Airtable-backed catalog of 500+ ASIC miners, so answers come from the catalog rather than from the model. Each lead is scored; qualified leads are handed to a salesperson, and the rest enter timed follow-up or are archived as cold.
What Qzentra was responsible for
Each stage of the workflow, on the layer where it runs. The conversation crosses two channels and comes back to automation after the handoff.
Inbound direct messages are the entry point. Each new conversation is captured through the Instagram messaging integration.
Qualification logic runs before the conversation goes any further, so only real sales enquiries continue.
Qualified conversations continue on WhatsApp, the channel the sale is closed on.
The AI layer understands text, voice notes, and images, so a photo of a machine or a spoken question is handled in the same conversation as typed messages.
Product questions are answered by retrieval over the Airtable-backed catalog of 500+ ASIC miners, not from the model’s memory.
Each lead is scored as the conversation develops.
Leads that score highly are routed to a salesperson. The system stops; a person continues.
Leads that are not ready enter timed automated follow-up. Leads that go cold are archived, so nothing sits unresolved in the channel.
The whole funnel runs as a single n8n workflow, so qualification, messaging, AI, retrieval, scoring, and handoff share one state and one place to change the logic.
Qualification happens on Instagram, before the conversation moves to WhatsApp, so only qualified conversations are carried into the closing channel.
Product answers are grounded in the Airtable catalog through retrieval. A catalog of 500+ miners changes; a model’s training data does not.
Voice notes and images are processed alongside text rather than rejected or diverted to a person.
Scoring decides when a salesperson is brought in. Everything before that point is automated; the handoff itself is deliberate and human.
Leads that are not ready receive timed follow-up; leads that go cold are archived. The channel does not accumulate unresolved conversations.
Named last, with the role each played. The capabilities are what the project demonstrates beyond its primary service.
Capabilities demonstrated
Discuss a project
Describe the channels, the knowledge the answers must come from, and where a salesperson should take over. That is enough to scope the system.