Freelance AI Agent EngineersFreelance AI Agent Engineers
AI Agents | LLMs, Computer Vision & Full-Stack Dev
89
Followers
AI Agents | LLMs, Computer Vision & Full-Stack Dev
AI Automation Engineer | Full-Stack Apps & Integrations
66x
Hired
4.9
Rating
156
Followers
AI Automation Engineer | Full-Stack Apps & Integrations
Engineering & Architect in AI and Product Manager
$50k+
Earned
33x
Hired
5.0
Rating
213
Followers
Engineering & Architect in AI and Product Manager
Cover image for Muddy Paws: a booking engine
Muddy Paws: a booking engine that turns "can I book?" into "you're booked" in 60 seconds Business: Muddy Paws Mobile Grooming, Lisbon, Portugal as Early Stage ! One-line problem solved: A solo mobile dog groomer was losing bookings and about 1 in 5 appointments to no-shows, so I built a booking flow that confirms and protects every slot with no manual back-and-forth. The client brief: InĂŞs runs a grooming van in Lisbon, alone, booking about 6 dogs a day, Tuesday to Saturday. Requests arrive through WhatsApp and Instagram DMs and get lost. She can't answer the phone while grooming. About 1 in 5 customers don't show up, which costs her the slot and the fuel. Appointment length depends on the dog's size and coat. She drives between neighborhoods, so not every time slot works. The goal: turn an inbound inquiry into a confirmed, paid booking with as little effort from InĂŞs as possible. Before and after Before: a DM arrives, InĂŞs replies hours later, they negotiate times, the customer forgets, the dog doesn't show, and the slot is gone. After: the customer picks their dog and services, sees only the slots InĂŞs can actually serve, pays a small deposit, and gets an instant confirmation. Reminders, rescheduling, and waitlist refills happen automatically. What I built: Fixing the front door Dog profile and service selection, with duration and price calculated live from size and coat. Address check against InĂŞs's service zones. Route-aware availability: slots respect her neighborhood-by-day schedule, service length, and travel buffer. Deposit to secure the slot, then an instant confirmation with calendar links. Guest checkout with no account needed. Fixing the follow-through: Reminders at 48 hours and 2 hours with one-tap confirm, reschedule, or cancel. Cancelled slots reopen and go to a waitlist automatically. A nudge for customers who start a booking and don't finish. An owner dashboard: today's route, a "Needs attention" queue, and hours saved. Built to work worldwide: currency, timezone, language (including Arabic right-to-left), units, and service zones are configurable, so the same engine can serve a groomer in Lisbon or a dentist in Toronto. How it maps to the criteria: Problem-solving impact: a complete path from inquiry to a paid, confirmed booking. Owner-effort reduction: confirmations, reminders, rescheduling, waitlist, and lead follow-up all run without InĂŞs. Craft and execution: mobile-first, branded, and tested end to end with seeded demo data. Storytelling: the demo video moves from a chaotic WhatsApp day to a calm dashboard with the hours-saved number. Process: I treated it like a real client brief: define the owner's quirks first, then design the booking logic around them (duration, route, deposits), then build in Lovable in layers, starting with the booking flow and then automations, dashboard, and global settings.
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119
Full-Stack Dev | AI Automation Architect | Startup Growth 🚀
$100k+
Earned
15x
Hired
5.0
Rating
270
Followers
Full-Stack Dev | AI Automation Architect | Startup Growth 🚀
AI-Native Full-Stack Development
$25k+
Earned
6x
Hired
5.0
Rating
123
Followers
AI-Native Full-Stack Development
Cover image for Crawrix.ai (https://Crawrix.ai) - AI Agent
Crawrix.ai (https://Crawrix.ai) - AI Agent that does SEO, GEO and AEO for you. Find out what AI says about your business, then fix it. More people now ask an AI assistant for a recommendation instead of searching Google. Most small businesses don't know if they appear in those answers. The crawlers behind them don't run JavaScript, so plenty of sites are invisible to them and nobody notices. Crawrix measures this. It asks AI models about the business and its category, runs search-grounded checks through Perplexity, and crawls the site for what answer engines read: AI-crawler access in robots.txt, llms.txt, structured data, sitemaps, and pages that answer real customer questions. The output is one score with a history, so an owner can see whether a change helped. A chat agent then works through the fixes. With a GitHub repo connected, it reads the codebase, writes the change and opens a pull request. It knows Next.js, Astro and other frameworks, and it can audit a live URL when there's no repo at all. Every write stops at an approval card that shows the diff first. Key features AI visibility score built from three parts: what models already know about the business, how it shows up in AI search, and how ready the site is for AI crawlers Ranked fix plan, with step-by-step guided cards for changes made by hand Chat agent that audits code and live pages, including Lighthouse runs in a sandboxed browser Pull requests generated from findings, each gated behind owner approval One agent, three ways to use it: the web app, a command-line tool (TypeScript and Rust builds), and an MCP server for Claude Code, Cursor and other MCP clients Scheduled re-scans that keep the score history current Connections to your own MCP servers, with OAuth and encrypted credential storage WordPress, Wix, Webflow and Framer integrations in progress, so sites without a repo get the same approve-then-apply flow
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