How I Built an AI Voice Receptionist That Actually Books Appointments (Under 800ms Latency) Hey everyone! 👋 70%+ of after-hours callers hang up the second they hear a voicemail beep—and book with the next competitor on Google. Alongside our platform work at Oqvera, I’m expanding my custom voice AI and automation services to Fiverr. Here is the exact production blueprint I use to automate inbound calls and eliminate missed leads: 🛠️ The Core Stack - Voice & Telephony: Vapi AI / Retell AI + Twilio - STT & Audio: Deepgram Nova-2 + Cartesia / ElevenLabs - LLM Engine: GPT-4o / Claude 3.5 Sonnet (Tool Calling) - Automation & CRM: n8n / Make.com + GoHighLevel / Cal.com ⚙️ How the System Operates 1. Sub-800ms Inbound Response: Instant, human-like voice handling with zero awkward robotic pauses. 2. Dynamic Live Scheduling: AI queries live calendar slots via API in real time, avoids double bookings, and reserves appointments on the spot. 3. Guardrailed FAQs: Answers pricing, location, and service questions with zero hallucinations. 4. Automated Post-Call Sync: n8n instantly pushes call transcripts to CRM, fires an SMS confirmation to the caller, and initiates warm transfers for emergency cases. 💡 Pro Tip Keep total round-trip latency under 1 second and always set up an SMS/transfer fallback for edge cases. If you want a ready-to-deploy voice receptionist for your business or clients: 👉 Check out my Fiverr Gig: https://www.fiverr.com/s/AGBz4qX For the voice builders here: Vapi or Retell AI—what is your go-to framework right now? Let’s talk below!

Excited to share an update on a project I've been building — an AI-powered receptionist for auto dealership showrooms. The problem it solves: dealerships lose leads constantly because no one answers fast enough — after-hours calls go to voicemail, web leads sit overnight, and by the time someone follows up, the customer's already talking to a competitor. What it does: → Answers calls & chat instantly, 24/7 → Qualifies leads (vehicle interest, trade-in, timeline) → Books test drives/appointments straight into the calendar → Checks live inventory availability → Auto-follows up on cold leads so nothing slips through → Hands off warm leads to a real rep with full context I've built it out for a client and it's now live in the testing phase — running real calls, measuring response times and booked appointments before we roll it out further. More updates soon as the pilot data comes in. Happy to answer questions if anyone's exploring something similar in their own niche 👇

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Hey everyone 👋 I’m Saad, an AI/ML Engineer and developer, and today I’m excited to introduce something I’ve been building: OQVERA. I didn’t want to create another website that simply says: “We build AI solutions.” I wanted to build something that shows what I can actually build. So OQVERA is becoming my public engineering portfolio — a place where I showcase real projects, AI systems, automation workflows, agentic applications, and the technical work behind them. What you’ll find inside OQVERA 👇 🤖 AI & Agentic Systems Multi-agent architectures, AI agents, RAG systems, LangGraph workflows, intelligent assistants, and LLM-powered applications. ⚙️ Automation & Workflows Business process automation, API integrations, AI-powered workflows, calling agents, and systems designed to remove repetitive manual work. 💻 AI Engineering & Software Backend systems, APIs, AI integrations, computer vision, intelligent applications, and production-focused engineering. 🔬 Case Studies I’m documenting projects not just by showing the final UI, but by explaining the problem → architecture → technology → implementation → outcome. Why I built OQVERA I want my work to speak for itself. Instead of telling potential clients: “I can build an AI agent.” I can show them one. Instead of saying: “I know automation.” I can show the workflow. Instead of listing 20 technologies on a CV... I can show what I built with them. That’s the direction I want to take with OQVERA. And yes — I’m building this with clients in mind. If you're a founder, business owner, startup, or team looking at ways to use: - AI agents - LLMs - workflow automation - intelligent customer support - AI calling systems - RAG / knowledge systems - custom AI software - internal business automation I'd love for you to check out what I'm building. 🌐 OQVERA: https://oqvera.netlify.app/ This is only the beginning. I’ll be continuously adding new projects, experiments, architectures, and case studies as I build.

Hey everyone! 👋 ​I wanted to share a complete real-world Voice AI build I just wrapped up: Alex, an automated AI Voice Operations Receptionist for a pre-owned auto dealership (National Motors Inc.). ​If you’re building voice agents for local businesses, lead generation, or appointment setting, here is how the whole stack works under the hood. ​🔥 What the Voice Agent Handles Live on Calls: ​Real-time Inventory Lookups: Queries the live inventory database (make, model, mileage, pricing, stock numbers) and answers caller questions instantly. ​Test Drive Booking: Captures caller details (Name, Phone, Email, Preferred Date/Time) and logs them directly to a Google Sheet. ​Instant Email Confirmations: Automatically triggers HTML booking confirmation emails via Gmail right after hanging up. ​Trade-in & Financing Routing: Collects trade-in specs and guides callers through pre-qualification flows. ​🛠️ The Tech Stack: ​Vapi.ai: Voice Engine (STT/TTS/LLM orchestration with custom system prompts). ​Make.com: Central Webhook Router handling function tool execution. ​Google Sheets: Functioning as both a live database for dynamic lookups and a CRM lead sheet. ​Gmail API: Instant email confirmations. ​⚡ Key Takeaways & Common Pitfalls to Avoid: ​If you’re trying to build something similar in Vapi + Make.com, keep these technical details in mind: ​Array Indexing Matters: Vapi passes tool calls as a nested array (toolCalls[1]). If you map fields as toolCalls[] inside Make.com, your JSON strings will break, resulting in "No result returned" timeout errors! ​Routing Function Calls: Always set explicit Filter Rules directly on your Make.com Router modules (e.g., function.name EQUALS book_test_drive) so different tool execution branches don't trigger at the same time. ​Return Clean Webhook Responses: Always return an explicit 200 OK status with Content-Type: application/json header and a results array containing the toolCallId. Otherwise, the AI assistant will time out waiting for backend confirmation!