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🚀 My Lovable Challenge submission: Airix Services
I built Airix Services, a Lahore-based AC repair and service business, to solve a simple problem: customers often have to call or message back and forth just to explain an AC issue, find an available time, and get their service booked.
With Airix, customers can explain what’s wrong with their AC, get guided through the booking process, choose from real available slots, and stay updated through confirmation.
Behind the scenes, the business owner can manage requests, appointments, technicians, and customer updates, while technicians can view assigned jobs and keep customers informed.
Built with Lovable.
🔗 Live project: https://airix-services.lovable.app/
I’ve also included demo access so the experience can be explored directly.
Thanks to @Lovable for the challenge!
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Not design only 😄🍝
A few photos from my latest cooking party in Prague — one of the things that actually inspired my Lovable Challenge project: https://on.contra.com/QhDrRw
I love design, I love cooking, and this project let me combine both ❤️
If you haven’t supported my submission yet, please head to my challenge post and leave a like or comment! ❤️🔥👇
Cooking Party Booking Experience ✨
Hi Contra Community ❤️ Taking part in a Lovable challenge feels like the perfect way to celebrate reaching 1,000 followers...
Such a cool inspiration source for a project 🍽️🔥
Enterprise RAG AI: Private Doc Intelligence
A fast-growing B2B software firm needed a secure, enterprise-grade Retrieval-Augmented Generation (RAG) system to query thousands of confidential internal documents (legal contracts, technical architecture specs, Notion pages, and employee SOPs) in real time without third-party data leakage.
Key Challenges:
High hallucination risk and irrelevant context retrieval using standard keyword search.
Managing multi-format files (scanned PDFs, tables, markdown) with strict access control based on user hierarchy.
Technical Implementation & Solution:
• Engineered an automated ingestion pipeline in Python with LlamaIndex and LangChain for semantic chunking and metadata preservation.
• Integrated a hybrid vector search architecture pairing dense embeddings (OpenAI text-embedding-3) with sparse BM25 indexing in Qdrant.
• Added a cross-encoder re-ranking layer (Cohere Re-ranker) to prune low-relevance chunks, reducing API token costs by 40%.
• Built robust Role-Based Access Control (RBAC) via FastAPI endpoints, ensuring departmental data isolation.
Results & Impact:
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• Attained 98% factual precision with traceable in-line page citations.
• Full on-prem Docker deployment ensuring 100% private data security.
I'd test revoking a user's document access mid-conversation. In your RBAC flow, do cached retrieval results get rechecked against current permissions before they're sent to the reranker or model?
I published my first Framer template. It’s free.
Raw Roast is a site for a coffee shop that roasts its own beans. The style is street-like: big hand-drawn lettering and black-and-white doodles.
What’s in it:
Home, About, Our Process, Gallery, CMS-based menu, Item page, Cart, 404, 401 and legal pages, Desktop, tablet and phone layout.
Links
Template in Framer: https://www.framer.com/marketplace/templates/raw-roast/
Live preview: https://raw-roast.framer.website/
Feel free to use it and change it for your own shop. If you do, I’d be glad to see the result.😍
Crisp, clean, and effective.