Freelancers using Twilio in MadridFreelancers using Twilio in Madrid
Founder of Kazfen | AI Agents, SaaS & Automation
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Founder of Kazfen | AI Agents, SaaS & Automation
24/7 AI Automation | Receptionist + Sales + Workflows
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24/7 AI Automation | Receptionist + Sales + Workflows
Cover image for ## Case Study 1: AI
## Case Study 1: AI Phone Receptionist **Tagline:** A 24/7 voice AI that handles calls, books appointments, and takes payments — without a human. --- **The Challenge:** Service businesses lose significant revenue when calls go unanswered outside business hours or during peak times. Hiring and training front-desk staff is expensive, inconsistent, and doesn't scale. Most small and mid-size operations simply miss calls — and those missed calls are missed revenue. **The Solution:** - Built a voice AI system using Twilio/Telnyx for telephony and Claude API for real-time natural language understanding, capable of handling inbound calls end-to-end - Integrated appointment booking logic directly into the call flow — callers can schedule, reschedule, or cancel without speaking to a human - Connected Stripe for in-call payment processing and n8n for backend orchestration (CRM updates, confirmation emails, Slack alerts) - Deployed via FastAPI with webhook handlers for call events, SMS follow-ups, and fallback escalation to a human agent when needed **The Results:** *(Estimated based on system design and architecture — not from a specific client engagement)* - **100% call coverage** — system designed to handle unlimited concurrent inbound calls with no wait time or missed calls - **~70% reduction in front-desk workload** — routine calls (booking, FAQs, payments) handled without human intervention - **Estimated 4–6 hrs/day recovered** per staff member previously tied to phone duties **Tech Stack:** `Twilio / Telnyx` · `Claude API (Anthropic)` · `n8n` · `FastAPI` · `Python` · `Stripe` · `PostgreSQL` · `Redis`
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Cover image for I built my own lead
I built my own lead generation machine because I moved to Denmark with zero network. I moved to Denmark. I didn't know anyone. I had no network. And I needed to find companies I could sell automation services to. I could spend hours manually searching for companies, finding emails, cleaning data, and sending messages one by one. Or I could automate the process. So I built a complete lead generation pipeline: → 🐍 Python scraper connected to Denmark's public government CVR database → 📊 Google Sheets synchronization to normalize and deduplicate companies → ✉️ Automated cold email workflow with 10 sector-specific templates using n8n → 📱 Daily Telegram reports with full pipeline statistics → 💼 Assisted LinkedIn outreach: 10 leads per day + a ready-to-send message And these are the numbers: 📈 19,334 leads in the database 📧 71% email coverage 🏭 10 sectors 🔢 34 industry codes ✉️ 25 automated emails per day ♾️ ~534 days of outreach without repeating a lead 💰 $5/month total cost — basically just the VPS. The interesting part isn't the scraper. It's not n8n. It's not Telegram. The interesting part is that a process that used to take hours of manual work can now run almost entirely on its own. And that's exactly what I want to build for companies: Find repetitive processes → design the system → automate them → measure the results. In the video, I break down the entire pipeline, piece by piece. If you're building something similar, let me know which part you'd like me to break down next. 👇 #Automation #AI #LeadGeneration #n8n #Python #ColdEmail #SalesAutomation #B2BAutomation #Denmark #BuildInPublic
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