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AI-Powered Email & Lead Automation Workflow
Built a smart AI automation workflow that turns incoming emails into structured, actionable business data — while keeping the AI process traceable and easier to debug.
The workflow automatically handles:
Email/Webhook → Data Extraction → Airtable → Validation → AI Processing → Human Review → Response → Team Notification → Reporting
I also added a dedicated logging layer before and after the AI call to improve reliability.
It can capture the input schema structure, model parameters, input/output hashes, timestamps, execution status, and error information without unnecessarily storing sensitive prompt content.
This makes it easier to identify edge cases between Airtable and OpenAI, troubleshoot failed executions, monitor AI behavior, and maintain a clear audit trail.
This type of system can be customized for lead management, customer support, sales inquiries, CRM automation, reporting, internal operations, and other AI-powered business workflows.
If your team is spending hours manually processing emails, updating databases, generating responses, or moving information between different tools, I can build a workflow around your actual business process.
#AIAutomation #WorkflowAutomation #MakeAutomation #AirtableAutomation #OpenAI #AIAgents #BusinessAutomation #EmailAutomation #APIAutomation #CRMautomation #NoCodeAutomation #ProcessAutomation #AIWorkflow #AutomationDeveloper #ArtificialIntelligence.
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 wrapped up an AI voice agent for a residential cleaning business 🎙️ It handles the full call end to end — checks service area, gives a live quote, checks real calendar availability, and books the appointment straight onto Google Calendar. Built with Vapi + n8n + Google Calendar API. Portfolio build, but fully working — swipe through the n8n workflow in the video 👀
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Behind the scenes on how our build process actually changed this month.
For most of this year we ran AI under a budget constraint, and that constraint quietly shaped everything. Which problems we pointed AI at. Which we did by hand. How much of a codebase we were willing to let a model read. Scarcity was the organising principle and we had stopped noticing it was there.
Then it lifted. Here is what actually broke.
The problem nobody warned us about. Freed from budget pressure, sessions got long and deep. A model will happily work a huge problem in one sitting. But the sitting ends — timeout, full context, or you go to bed. And everything it worked out along the way vanishes. The dead ends it ruled out. The constraint it discovered in hour two. The reason it chose one approach over another.
When sessions were short, we held that context in our heads. When they got long, we couldn't. We started losing more to forgotten context than we had ever lost to rate limits.
What we built to fix it:
Exit interviews. Before closing any meaningful session, we prompt the model for a handover briefing — what it did, what it decided, what the next instance needs. Not a commit message. An actual briefing. That output becomes the opening context next time. This is the single highest-leverage thing we changed and it took an afternoon to adopt.
Browser session stability. Long builds kept dying when whoever was driving switched tabs. Moving active sessions into the Chrome extension fixed it. Unglamorous. Recovered more hours per week than any clever prompting.
Logic only, no UI. We kept fighting the same battle — strong architecture and logic coming back attached to cluttered, incoherent interfaces. So we stopped asking. We now prompt for logic and architecture and explicitly block frontend generation. Structural foundations go to a human who builds the interface natively. Restricting the model made the output better, which is not what I expected.
Two-stage handoff. Fast rough first pass to kill ambiguity, often straight from a client scope document — one was 125 pages fed through in a single stretch. Then a different person takes the careful second pass.
The part I did not expect. More capacity meant more verification, not less. We increased the QA burden at the same time we removed the ceiling, deliberately, because an agentic system will always find something to report and the report will always sound like progress.
The tools were the easy part. The reorganisation was the work.
infiniteup.dev
The Bottleneck Moved: How We Rebuilt Our Workflow Around MCP and Fable - InfiniteUp
We removed our AI capacity ceiling expecting to get proportionally faster. Instead the bottleneck moved somewhere less convenient. How InfiniteUp rebuilt its development workflow around MCP and Fable.
budget shaping the whole process, good honest writeup