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I designed and built an AI-powered lead management automation using Make.com to streamline the process of receiving, analyzing, organizing, and communicating lead information.
The workflow starts with a custom webhook that receives incoming data, then searches Google Sheets to check existing records. OpenAI analyzes and classifies the information, while JSON parsing converts the AI response into structured data for reliable processing.
Using conditional logic, the system automatically determines whether a lead is new or already existing. New leads are added to Google Sheets, while existing records are updated. Automated Gmail notifications are then triggered, and the results are merged and sent to Slack for team visibility.
Key Features
Webhook-based lead capture
AI-powered data analysis and classification
JSON data parsing
Google Sheets database management
Conditional lead routing
Automated Gmail notifications
Slack team notifications
End-to-end workflow automation
Tools: Make.com, OpenAI, Google Sheets, Gmail, Slack, Webhooks & JSON.
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Normal work disappears. Genuine judgment surfaces.
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I didn’t want to build another prettier scheduler. The problem I wanted to attack was the handling around the booking.
Routine requests can continue toward booking. Genuine ambiguity reaches the owner as one framed decision. Unsafe or wrong-fit requests stop instead of being forced into the normal booking path.
I also built a deterministic test harness and a playable Morning Shift proof mode: three fictional requests enter the same routing rules, but only one requires a human decision.
The part I’m most excited about is that the challenge itself became reusable. We captured the process from brief → working flow → adversarial testing → cinematic donor → bounded visual integration → playable proof → final freeze.
Built in Lovable.
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Built an automated lead workflow that takes an incoming request, organizes the lead in Google Sheets, notifies the business, and sends the customer a confirmation. The system reduces repetitive manual steps and keeps incoming leads organized as they move through the process.
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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