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The US pulling Anthropic’s models creates an immediate, billable need: AI provider abstraction layers.
Every company running on a single frontier API now has a single point of regulatory failure. Independent developers who can build provider-agnostic middleware — routing between OpenAI, Anthropic, and open-source models based on availability and compliance status — are solving a problem that just became urgent today.
The pitch is simple: “What happens to your product if your AI provider gets pulled tomorrow?” Most teams don’t have an answer. You can be the answer.
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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THREADLINE ATELIER
A premium booking experience for a custom fashion studio in Lagos.
I wanted to solve a simple problem: when someone asks, “Can I book with you?”, the designer shouldn't have to spend the next hour going back and forth about availability, measurements, event dates, and scheduling.
Threadline Atelier turns that into one smooth flow:
Inquiry → Design Brief → Availability → Booking → Deposit → Confirmation
Customers can choose what they're making, share their occasion and event date, upload inspiration, select a real available consultation time, pay the consultation deposit, and get booked.
For the designer, it reduces manual coordination, follow-ups, and scheduling headaches.
Visually, I wanted it to feel like a luxury fashion house with a digital experience editorial typography, contemporary African fashion, cinematic motion, and subtle thread-inspired interactions.
The signature moment:
“YOU'RE BOOKED.”
This is a well structured website but the booking experience is what I looks good.
Building AI for healthcare leaves zero room for error.
I’m currently collaborating with an incredible team on Raphald AI, a medical detection application. Building the systems for a project with stakes this high is a massive reminder that the underlying backend architecture matters just as much as the machine learning model itself.
When integrating diagnostic AI, your API endpoints cannot drop requests, and your database workflows demand absolute integrity. You aren't just passing JSON payloads; you are handling critical, real-time workflows where stability is non-negotiable.
Engineering these systems continues to shape my approach to building robust Python backends. If you are developing a product that requires reliable AI integration or rock-solid FastAPI infrastructure, check out the newly updated services on my profile. Let's build something that works when it counts.
AI SaaS Intelligence Platform
