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Right before 2025 ended, I finished this really nice project for a German SaaS company. They wanted a landing page that would blow their clients' minds and help them make new clients, with 3D animation and lots of fancy details.
I enjoyed working on this and the client (and their leads) loved the result. Everybody wins! 😍
Here's the final result.
And the deets on the case study page.
Thanks for reading!
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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Built this free SaaS portfolio template in Framer a few months ago, and it’s been amazing to see the momentum it’s gaining! 🚀
It’s now ranked #18 in Framer’s Marketing category, bringing in more visibility, views, and commissions.
I’ve shared the full case study here, covering the project and how it came together.
Take a look if you’re curious! 🙌
This is amazing Fawaz! 🚀 Ranked #18 in Framer's Marketing category is huge! Love how you turned a free SaaS template into visibility + commissions. That's smart product thinking. Can't wait to read the full case study, congrats on the momentum!
Before you add AI to your product, find the screen where your users already copy and paste.
That's usually where the real AI feature lives. Not a chatbot in the corner, but a step people already do by hand: summarising a ticket, drafting a reply, tagging an upload, pulling fields out of a PDF.
Here's the order I follow when a founder asks me to add AI to an existing SaaS:
1. Pick one workflow, not "AI everywhere." Watch where users leave your app to paste something into ChatGPT and come back. That round trip is your feature.
2. Keep the model behind your own API. One service owns prompts, retries, logging and cost limits, so you can switch providers without touching the frontend.
3. Make it tenant-aware from day one. Each customer's data, prompts and usage stay scoped to their account, or the first enterprise security review will stop you.
4. Ship a draft, not a decision. Let the AI suggest and the user approve. You earn trust and get real feedback before you automate anything.
5. Measure accepted output, not tokens. If users rewrite most of what it produces, the prompt or the context is wrong, and that's your real roadmap.
Most AI features that flop didn't fail because of the model. They were bolted onto the wrong step.
What's the one task your users still copy into ChatGPT?
Watching where users paste into ChatGPT and come back is such a practical way to find the real feature. Most teams start with the chatbot instead.
