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Automated vehicle inventory scraping and reporting system. Scrapes dealership websites for inventory data, processes and structures the output, and generates daily reports.
Technologies: Python, Playwright, BeautifulSoup, SQLite, cron
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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I built a comprehensive sales tracking
spreadsheet for an engine oil business
that automatically tracks daily sales,
calculates profit per transaction and
labels each sales. The client can instantly see their most
profitable product, total revenue, total
costs and net profit for any period
without any manual calculations.
This is exactly the kind of clarity every small business owner needs — being able to see profit per transaction and top products at a glance without manual calculations is a game changer. Great work on the performance labelling too! 🙌
New products need content.
But between an idea and a finished photoshoot, there are often weeks of preparation and significant budgets.
That's why I'm putting together a package that includes:
— 10 product photos
— 2 short videos
— 1 commercial-style video
So brands can get ready-to-use content for a product launch.
I'd love to hear your thoughts:🤩
How do you think a package like this could benefit a brand? What challenges or goals could it help solve?
I can see this being especially valuable for startups and early-stage products.
One of the biggest launch challenges is having enough quality content for different channels from day one. A package like this could help brands move faster and test messaging, creatives, and...
Multi-Agent AI & RAG Knowledge System for Real Estate
The challenge: Design a complete agentic AI architecture for a real-estate marketplace — one capable of serving buyers, sellers/brokers, and document-verification workflows through specialized AI agents, grounded in real data rather than free-floating LLM responses.
What we built: A full multi-agent system design comprising a fleet of specialized agents (a buyer-facing conversational concierge, a seller/broker AI co-pilot, a document/listing verification agent, and a pricing-valuation agent), coordinated through a central model-routing and orchestration layer. The system is grounded by a Retrieval-Augmented Generation (RAG) pipeline spanning multiple structured knowledge bases (legal/regulatory corpus, market pricing data, locality/infrastructure data) plus a graph-based knowledge layer linking listings, agents, leads, and locations for relationship-aware retrieval — with human-in-the-loop approval gates on consequential actions.
The outcome: A complete, implementation-ready technical architecture covering agent orchestration, RAG pipeline design (vector + graph retrieval), model-routing strategy, and safety guardrails — the kind of specification an engineering team can build directly from.
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