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More on Atlas, the MVP we built for an AI builder studio, where fintech teams can:
→ Create agents
→ Test them on real datasets
→ Ship workflows they can actually trust
We led product design end-to-end, focusing on a core flow:
1. Agent playground (testing + iteration)
The focus wasn’t just on designing interfaces, but it was making AI workflows understandable, reliable, and usable in a compliance-heavy context.
A core problem of AI systems is that they are inherently abstract:
→ Multiple models,
→ Schema-driven outputs,
→ Non-deterministic behaviour.
So we focused on simplifying this into a clear mental model:
Define → Run → Review → Refine
🧠 Our Product Design approach
1. Schema-first UX
Instead of exposing model complexity, we designed around structured outputs. Users define what they want, not how the model works.
2. Playground over pipelines
Rather than complex flow builders, we introduced a playground environment where users can:
→ Run agents on real documents,
→ See outputs instantly,
→ Iterate quickly
3. Making outputs trustworthy
4. Reducing cognitive load
🚀 Outcome
A working MVP where teams can:
→ Configure agents using structured schemas,
→ Run them on real-world financial documents,
→ Review outputs and refine workflows before deployment.
All while keeping the experience simple, clear, and production-oriented.
Still early, but excited about shaping how AI tools can feel more usable, especially in high-trust environments.
Reach out to us if you would like to know more! 🚀
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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