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AI-Assisted Operational Alert Prioritization: Created a concept for an AI-assisted operational alert dashboard designed to help teams prioritize high-impact issues more efficiently. The system focused on improving operational visibility, reducing alert fatigue, and surfacing the most urgent risks across multiple workflows and Slack channels.
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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Just in time for Halloween! 🎃🔮 Excited to share this mystical tarot reading app concept, inspired by a little spooky season magic. Would love to hear your thoughts! ✨
Excellent work!
Business data often lives across payment, analytics, CRM, advertising, and inventory platforms. Metrik brings those signals together in one dashboard built for startups and small businesses.
See revenue, orders, customers, and business health at a glance. Compare advertising spend and ROAS across channels, explore visual KPI cards, and use smart alerts to spot unusual changes and potential risks.
The interface pairs a clean layout and rounded components with soft gradients, colorful analytics, and clear visual hierarchy, helping make business performance easier to follow.
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.
