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 👇

What if multiple AI agents could work together to turn a game concept into a playable prototype? We're exploring a multi-agent AI system where each agent handles a specific part of the game development pipeline, from planning and 3D asset creation to level assembly, gameplay implementation, and testing. 🤖 How the system will work Our proposed agent team includes: - Project Manager Agent: Understands the brief, defines scope, and coordinates tasks. - Game Designer Agent: Plans the level, mission objectives, gameplay rules, and spawn points. - 3D Asset Agent (Blender): Creates or prepares 3D models, materials, and textures. - Environment & Level Agent (Unreal Engine): Assembles modular assets and builds the game environment. - Gameplay Logic Agent: Implements gameplay systems using Blueprints and C++ where needed. - QA & Testing Agent: Runs tests, identifies issues, and validates gameplay. - Documentation Agent: Tracks changes, decisions, errors, and development progress. ⚙️ Proposed technology stack - Claude Code + Claude Opus: Reasoning, planning, coding, and agent coordination. - Blender MCP + Unreal MCP: Connecting AI workflows to the respective development tools, subject to integration testing. - Unreal Engine 5.8: Level assembly, gameplay, physics, and rendering. - Python: Task orchestration, structured specifications, and automation. - Git + Git LFS: Version control and recoverable checkpoints. - Unreal testing framework: Gameplay validation and regression checks. 🔄 Development workflow Game Brief → Planning → Asset Creation in Blender → Import into Unreal → Level Assembly → Gameplay Integration → Testing & Validation → Playable Build Each stage will produce trackable outputs, with logs, checkpoints, and bounded repair attempts to help make the process reproducible and recoverable. 🏗️ Our initial goal We're starting with a small, playable military-style prototype featuring an industrial outpost, two teams, a capture-point objective, one weapon, health, reload, respawn, and up to four bots.

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! 👋 One of the biggest leaks in almost every local service business, clinic, or agency isn't client acquisition—it's speed to lead. When a prospect calls after hours or during a busy lunch rush and hits voicemail, over 70% won't leave a message. They just move on to the next business on Google. Alongside our work over at Oqvera, I’m expanding my skills and services directly to the freelance space on Fiverr to help more teams implement custom voice agents and workflow automation. Here is the exact breakdown of how this production voice architecture works under the hood so you can build or implement this for your own clients: 🛠️ The Tech Stack - Voice Engine & Telephony: Vapi AI / Retell AI + Twilio (SIP Routing) - Speech-to-Text & TTS: Deepgram Nova-2 + Cartesia / ElevenLabs - Brain / Reasoning: GPT-4o / Claude 3.5 Sonnet (with strict function calling) - Backend & Workflow Automation: n8n / Make.com + FastAPI webhooks - CRM & Scheduling: GoHighLevel / HubSpot + Google Calendar / Cal.com ⚙️ How the Workflow Operates 1. Low-Latency Inbound Handling: When a call hits the Twilio number, it routes to Vapi/Retell. Using Deepgram and Cartesia keeps the latency under 800ms, eliminating that robotic 2-second pause that makes callers hang up. 2. Knowledge Base & Guardrails: The system prompt is loaded with the business’s services, pricing guidelines, location, and operating hours. If a question is outside the scope, it gracefully deflects or offers to take a message rather than hallucinating. 3. Live Calendar Tool-Calling: When a caller says "I'd like to book a slot for Thursday afternoon," the LLM triggers a custom tool that pings the Google Calendar/Cal.com API, checks available slots in real time, and proposes exact times. Once agreed, it books the appointment instantly. 4. Post-Call CRM & SMS Pipeline (via n8n): The moment the call ends, an n8n webhook triggers: - Pushes the recording, full transcript, and structured summary to the CRM (GoHighLevel/HubSpot). - Fires an automated SMS confirmation to the caller with the appointment link and address. - If the caller mentioned an emergency keyword, it triggers a live warm transfer to an on-call manager.

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!