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Flow that automates Vapi calls from Airtable, routes CRM updates or follow-ups based on responses.
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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🚀 I built AutoPilot by NoCodeMinute on Bubble to help businesses automatically generate and schedule social media content across platforms in minutes instead of hours.
It uses a simple subscription model where customers pay monthly to automate posting, save time, and scale their marketing with AI-powered workflows.
This is awesome Jason!!
I built VeriVault using @Bubble , an enterprise-grade document portal designed to replace insecure email attachments with a secure, AES-256 encrypted 'handshake' between law firms and their clients. VeriVault integrated with Stripe to handle secure recurring billing and automated seat provisioning ,admins can manage their subscription directly from Settings & Billing dashboard.
🚀 VeriVault targets the "Missing Middle" of legal tech.
🚀 Value Prop: We reduce "document chasing" time by 40% through structured requests.
🚀 Monetization: SaaS model with three tiers (Basic, Pro, Enterprise). We capture high-value leads via a dedicated "Talk to Sales" channel for customized firm-wide deployments.
participated in :January Challenge #BubbleBuildsRevenue challenge.
Really clean execution 👏 Curious — what was the biggest challenge building secure document workflows in Bubble? Always interesting to see complex SaaS use-cases done well.
When I joined the team, social media metadata and posts were being uploaded manually. The process was repetitive, time-consuming, and not scalable. It limited the volume of data we could handle and increased the risk of inconsistencies and human error.
I proposed replacing the manual workflow with an automated ETL pipeline. Using Python and version-controlled workflows in GitHub, I built a system to extract data directly from social media APIs, transform and standardize the datasets, and load them into BigQuery.
The result was a fully automated, scalable pipeline with validation controls and traceability. What used to require daily manual intervention became a reliable production process capable of handling large volumes of data efficiently.
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