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Built an AI-powered callback system that uses voice automation to engage leads, collect key information, and trigger follow-up workflows, helping businesses respond to prospects faster without relying entirely on manual calls.
The network for creativity
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Designed a modern Health Biomarkers Dashboard for Vital Trend Health.
Clean and insightful UI that turns complex medical data into an easy-to-understand experience. Features clear optimal vs sub-optimal indicators, detailed trend graphs for key biomarkers (Hemoglobin, ALT, HbA1c, etc.), overall health overview, and a smart AI health assistant that provides personalized guidance and explanations.
Really nice work!
: Hi everyone!
AI Lead Qualifier — chat agent that qualifies leads before your manager picks up the phone.
THE PROBLEM
78% of buyers choose the vendor who answers first. Most inbound leads wait hours for a human.
THE SOLUTION
An AI agent answering every lead within seconds, 24/7: short dialog → structured data (need, budget, timeline, contact) → A/B/C grade → instant Telegram alert with a full lead card. Everything logged to Google Sheets.
BUSINESS IMPACT
• Response in seconds, not hours
• 50–70% of routine questions off your sales team
• Structured lead card for every inquiry
• Self-hosted — no monthly SaaS fees
PROVEN IN A LIVE TEST
From "can you make a website for my store?" the agent extracted need, timeline, budget, name and phone in 2 messages, scored the lead class A, and alerted the manager — automatically.
Built on n8n + LLM + Telegram. Details in my profile — DM me.
Does someone on your team lose a day or two building the sales report every week or month?
That was the starting point at Mokobara, the premium travel brand. Pulling sales across all their stores, checking returns, reconciling the numbers and emailing each manager took 1 to 2 days every cycle, with 8+ hours of that spent just reconciling.
I built them an automation on Make.com a few months ago. It has run on its own ever since, weekly and monthly:
Make pulls every sales record from BigQuery, 100K to 250K per cycle. That is too many for one request, so it reads them page by page and stitches them back together.
It calculates the numbers the team actually uses: net sales per store after returns and discounts, return rates and the change against the last period.
It builds a CSV with the full breakdown and a short email summary you can read in 60 seconds.
It reads a Google Sheet of store representatives and emails every one of them the report for their own store.
The result: the reporting problem is gone. The report went from 1 to 2 days of manual work to fully automatic, and the 8+ hours of reconciliation dropped to zero.
Two things I would do the same way again:
Calculate "net sales" in the automation, not in the warehouse. The business rule for what counts as a net sale is not what the raw data stores.
Send people their slice, not the whole report. One report for everyone gets skimmed by everyone.
The figures in the image are placeholders, the real ones stay with the client.
What report is your team still building by hand? Tell me where the data lives and I will tell you how I would automate it.
