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š Building Scalable Digital Products for Businesses & Startups
At ASCANXlabs, we help companies transform ideas into production-ready software.
Our expertise:
ā¢ Custom SaaS Platforms
ā¢ Mobile Applications
ā¢ AI-powered Solutions
ā¢ Business Automation Systems
ā¢ Marketplace Platforms
Recently, we built ASCANX ā a PropTech marketplace connecting students with verified housing providers through a scalable mobile platform.
Looking to build a digital product, automate operations, or launch an MVP?
Let's connect.
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
Related posts
Building with Wonder š
I've had the idea for HirePilot sitting in my idea bank for quite some time. When I saw the Wonder Challenge, I thought it was the perfect opportunity to finally bring it to life.
With only 3ā5 hours, learning a completely new tool while balancing my day job, I knew I wouldn't be able to build everything I had envisioned. My goal shifted from creating a perfect product to exploring how Wonder could fit into my design workflow.
Instead of treating Wonder as just another UI generation tool, I used it as a collaborative design partnerāiterating on product ideas, refining layouts, experimenting with shaders for motion and depth, and exploring MCP to make the experience feel more connected. While I wasn't able to fully complete the interactive experience I had planned, it gave me a glimpse of what's possible when design and AI work together.
About HirePilot
HirePilot is an AI career co-pilot that helps candidates prepare for the exact interview they're about to take. By combining a resume, job description, target company, role, and experience level, it creates personalized interview preparation with resume optimization, company-specific mock interviews, actionable feedback, and career insightsāall in one place.
This isn't the final version I imagined, but it's a meaningful first step. I'm looking forward to taking HirePilot further, making it fully interactive, and pushing Wonder even more in the next iteration.
#BuildWithWonder #Wonder #AI #DesignChallenge
Stop Building AI Demos. Build Store-Ready Products. š
Most AI photo projects end up as simple wrappers or half-baked demos. I wanted to build something real, production-ready, and fully monetized.
š Meet Atelier ā a production grade AI Photo Studio built for the App Store and Google Play.
Packed with 13 heavy-duty AI toolsāincluding AI Enhance, 4x Upscale, and precise Background Removal (as showcased in ai-photo-1.png) - Atelier processes images fast and securely.
ā” Engineering the Experience:
- The Frontend: Built an ultra-smooth cross-platform UI with Flutter, featuring an interactive Before/After comparison slider (ai-photo-2.png).
- The Creative Engine: Integrated 6 distinct art styles and a custom style prompt engine for infinite generation flexibility (ai-photo-3.png).
- The Backbone: Engineered a secure Go backend that interfaces with AI image models, handles fast data delivery, and manages strict receipt-verified In-App Purchases (IAP).
No generic templates, no lagging interfacesājust a complete, commercially viable mobile ecosystem.
Need an engineer who builds real, revenue-generating AI products instead of just placeholders? Letās Discuss!š„
#ProductDesign #MobileDevelopment #B2BSoftware #AIAutomation
Perfect
Would you let an AI write SQL against your production database?
I wouldn't either. So when I built Quanta ā a workspace where you ask questions in plain English and get back a live dashboard ā the model never touches your data directly.
Every query it generates is SELECT-only and runs read-only. If one fails, the system feeds the exact database error back to the model and lets it correct itself once before anything reaches you. Credentials stay encrypted with AES-256-GCM and never leave the vault.
Connect Postgres or MySQL, switch between Gemini, Claude, and GPT, and build dashboards from 12 chart types. It's multi-tenant now too. Orgs, roles, and per-workspace AI spend caps.
Curious how other devs handle the "LLM + real database" problem. What guardrail would you add?
ā¢ Jul 5
The self-correction loop is such a smart touch. Only thing I'd add is a statement_timeout + forced LIMIT so a valid-but-heavy query can't tank prod. Ideally run against a read replica. Great build.
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