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Turning CRM Chaos into a Revenue Engine
Built an AI-driven HubSpot × Apollo automation that runs outreach, enrichment, and engagement on autopilot, every 6 hours, zero manual work.
💰 Why it matters:
• Faster lead enrichment = higher reply rates
• Personalized outreach at scale
• Clean, always-updated CRM
• Sales teams focus on closing, not admin
This is how modern teams save hours, increase conversions, and unlock predictable revenue with automation + AI.
Curious 👀
Would you trust AI to run your outbound engine, or do you still prefer manual control?
👇 Let’s discuss
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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WIP! Designing a hero section for an AI automation brand.
Nothing final yet — just testing background visuals.
Which one do you like more — A or B?
9 voted
60%
6 voted
40%
15 votes
Closed
Good automation doesn’t just save clicks. ⚡
It makes work easier and faster.
Here’s what it means in simple terms:
📩 Form fills go straight to your system (no copy-paste)
🧠 Leads can be checked automatically before you reply
🗓️ People can book calls without back-and-forth messages
🌐 Websites guide users clearly to the next step
🔄 Follow-ups happen automatically so no lead is missed
The best no-code systems aren’t fancy.
They eliminate the small, time-consuming tasks that drain your day.
That’s where Webflow, automation, and AI help the most. 🚀
💬 What’s one task you’d automate in your work?
#NoCode #Automation #Webflow #WorkflowAutomation #Zapier #Bubbleio #Framer #Productivity #UXDesign #BuildInPublic #StartupTools #Contra #NoCodeTools
Automating the small repetitive tasks really does make a big difference.
Currently building autonomous AI systems that actually survive production — not just the demo.
Most AI projects work beautifully in a controlled test and then quietly fall apart on real data, edge cases, and concurrency. I focus on the unglamorous engineering that closes that gap: data validation pipelines, boundary checks, error handling, and knowing exactly where deterministic logic should own the decision instead of the model.
Recent work includes agentic workflows built with Python and FastAPI, an LLM reasoning layer for the genuinely ambiguous decisions, and automation orchestrated through Make with MCP. My background is technical support and operations, which means I design for the failure case first — I've spent years watching systems break in ways their builders never anticipated.
If you have an AI pilot that impressed everyone in the demo and then stalled on the way to something dependable, that gap is exactly what I build for.
Open to Applied AI / AI automation projects.
Keep it up!