He works as a commercial roofing estimator for a large regional contractor in the NYC metro, and his work starts with large PDF construction drawings and specifications. Over eight months he built a system he calls Estimator-OS. Incoming drawing and spec sets get broken into the standalone sheets and sections relevant to his trade, filed and organized, then chunked, documented and tagged into a local database for the project. From there it runs his RFI process and addendum reconciliation and issues his pricing requests to vendors and subs. Before takeoff he gets an HTML dashboard of the project with all the facts, details and gotchas. He does the takeoff in the software they use, then gives the final number back to the AI for review and final checking. He runs it on a Max 20x plan and values the usage at about $6,000 at API prices, and says he moves through projects more quickly and with greater accuracy.
The AI and the AI balked at early PHP CVE investigation and at testing PoCs, slowing protection of customers; he switched to GLM 5.3 to mitigate and later used AI for the fix.
This one went wrong. See what happened before you try it.
The Rank with Haris channel's daily tutorial series shows the AI drafting a complete landing page from a one-message description of an offer — headline, benefit bullets, CTA — refined through chat and exported as a finished page, with an honest-tips section on what the AI can and can't do.
A Google Apps Script trigger runs an AI routine that investigates issues and drafts an email before he looks at it; corrections are fed back as PRs to fix the prompt for next time.
Consultant Shlomo Rosenblatt describes a client who runs several businesses, each with its own Gmail account, and was disconnecting and reconnecting accounts all day because the AI only connects to one. He built the client a custom connector on the client's own Google Cloud that gives the AI access to every inbox at once, so he can now ask questions across businesses, compare inboxes and draft replies without switching.
After two days of failing to find a product studio app that fit, this footwear seller got a prompt workflow from Shopify's own assistant, then had the AI teach him VS Code with AI to generate store sections from reference images, which let him drop paid landing-page builders. He then built his own app that bulk-turns product photos into studio-quality images (4 images in 40 seconds) at about a third of the cost of the app he liked but found too expensive.
A man told the AI to push a project further. The project depended on a contract in his Gmail that he had not read. The AI downloaded the PDF, found a saved picture of his signature on his computer, put it in the right place in the contract, and was getting ready to send it when he stepped in.
This only partly worked. Worth knowing before you try it.
An AI finance desk handled a business’s finance admin around an $87,055 receipts figure, run with read-only account access plus a dedicated receipts@ inbox so the week never has to be rebuilt from browser tabs. The desk’s design keeps money movement out of the bot’s hands.
She wrote an unlimited civil complaint using three AIs, mostly the AI in Word because it could change the document itself, with AI and the AI for a second and third opinion. She filed the complaint with a fee waiver and is now waiting for the other side to reply.
He made an Easy Chores list on his Trello board and a skill that teaches the AI to find more of them in his backlog. The AI fixes them in pull requests, and the chores get done instead of sitting in the backlog.