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KTO / Articles
Practical articles on AI systems, commercial real estate, and building the business.
Cap rate prices a building, but debt service coverage ratio (DSCR) decides whether its income can carry the loan. Here is how lenders use DSCR to size a commercial loan, and how to explain it to a client without over-explaining.
On Tuesday night, a scheduled AI run hit a check that told it to stop. It edited the check, ran the tests, and sent anyway.
Splitting every ad into endless variants is advice written for people who do not pay per render. Here is how to A/B test content on a budget using cadence, a two-creative rule, and a simple test ledger.
A new check stopped Red from filing my second AI's work sessions from Sunday until Tuesday night. The check was right. The records had been wrong since August.
Instagram now decides who sees your posts by interest, not by your follower count, so a small account can reach the right buyers. Here is how the interest graph works, and how to build a simple AI content engine that keeps it fed.
On Wednesday, several copies of Red were working at once and all of them needed the same spot in line. It took three fixes, and part of the problem wasn't the line at all.
Meta and Google both strip the targeting most agents rely on the moment an ad counts as housing, and the demographic filters simply disappear. Here is what you can and cannot target, and how to plan a campaign that still reaches the right buyer.
A publishing pipeline can run perfectly and reach nobody. Here are the three numbers to check before you make another video, and the three habits I am testing this month to move them.
This week my AI got more room in one place and less in another: standing approval for two kinds of email, and a stricter rule for anything written in my voice.
Compare ChatGPT, Claude, and Gemini on your own recurring tasks, including accuracy, editing time, data handling, and the integrations you need.
My main conversation with Red moved out of Slack and into a dashboard we built for it. Before switching, we tried to break it.
Prepare a first-time buyer consultation with an agenda, questions about priorities and financing, a process overview, and an agreed next step.
Every post my system publishes is checked against the exact words I approved. For about two weeks, those checks kept passing on posts almost nobody could see.
Turn approved buyer-consultation notes into a draft summary, search criteria, and follow-up tasks, then check each item before adding it to your CRM.
Our AI admin assistant had stayed inside the approval rules for 46 runs. We moved her into her own space anyway, with a much smaller view of the business.
Work through a small multifamily property from the rent roll to financing. Includes a hypothetical 12-unit example and questions to check with the lender.
Red was turning every unfinished follow-up from my work sessions into a task for me to confirm. More than half of them ended up cancelled, so I changed the rule.
Help a remote buyer get oriented with clear questions, an area overview and agreed updates. Keep the client and referring agent informed within the arrangement.
My monthly report gave me three clean conclusions, and each one was wrong for a different reason. The same week, Red made a confident mistake of its own.
What Anthropic’s real estate task data can and cannot tell us, and how to review AI research with clients or test a workflow in your own business.
Red stress-tested my plan for the Academy and scored it 2.4 out of 5. I built it my way anyway, and Red wrote the disagreement down plainly.
Questions to review for a first retail lease, from the business’s occupancy budget and NNN charges to proposed terms, lease review, and representation paperwork.
For two weeks, the emails my website sends were failing, and four different parts of the system said they went out. One quiet log had the truth.
Prepare a listing appointment around the seller’s plans, pricing evidence and a clear marketing proposal. Use questions and a written recap to guide the conversation.
I asked whether my scattered tools meant I needed more AI specialists. Red's answer was no: there were 114 tools, and none had an owner recorded.
Use a few clear questions to organize new inquiries, suggest follow-up priorities, and hand conversations to a person for review.
I sent Red an article on the CIA's red-team techniques and asked for the same thing for my ideas. The first test run sent an idea back for revision.
A post claimed an AI tool turned 365 photos into an eight-minute video. We built a repeatable version for my team, then kept its self-serve side switched off.
Compare the property and lease questions involved in retail, office, and industrial work before choosing a commercial specialty to study.
Phase two meant expanding what Red could change inside the live system that holds my tenant and landlord records. When I said go, it stopped and asked for rules.
When a seller is meeting other agents, ask what they want to compare and explain your proposed work. Practice a response that respects their decision.
Red wrote a fresh batch of video scripts every day, and each batch waited for my approval. Six of the eight expired before I got to them.
Sample ChatGPT prompts for real estate follow-up messages, with steps to add context, edit the draft, and check it before sending.
This week I launched a public series about building Red. The rules for what those posts would never do came first.
Red drafted a text for me without checking it against the profile of how I write. I found out by asking one question.
An introduction to the income approach to commercial property valuation, with NOI, cap rate, and a hypothetical calculation explained step by step.
Red recommended against installing a popular open-source toolkit for AI agents and adapted five of its ideas instead. A sixth idea was already built.
In one day, Red got a written rulebook, a daily check on its own health, and a second opinion on approval requests. A rehearsal nobody asked for caught two mistakes.
A sample weekly prospecting schedule for preparing outreach, following up on conversations, publishing useful information, and reviewing the week.
I told Red to go ahead and implement all six of its suggestions. For three of them it had no sources, so it refused to make anything up.
Just after midnight, I asked a session that had already died whether we had the plan. The specialist that came out of it asks questions and keeps track of patterns.
Give an investor the main facts, explain what matters and keep the supporting records ready. A practical structure for deal emails and conversations.
A week in, I asked Red to audit itself. The gap between the plan and what was actually set up is where its rulebook came from.
The day Red moved into a home of its own, every persona in our task app got a rule about what it could do without asking. Red's own rule was read-only.
Turn approved listing details or a market note into social posts, email drafts, and video scripts, with a review step before publishing.
Start a commercial prospecting list with local businesses, property records and useful questions. Verify the facts before suggesting a property opportunity.
Review the parts of your workday that leave you exhausted, then consider changes to scheduling, outreach, and recovery time. Includes practical planning questions.
Understand what NOI includes, what it excludes, and how to calculate it from property income and operating expenses. Includes a worked retail example.
Turn a recurring task into a reusable template, an AI draft, and a review step. Start with one workflow and check whether it saves time.
A five-part discovery-call outline with sample questions, a next-step invitation, and a short practice routine for agents who prefer to prepare.
Browse the current article archive by AI, commercial real estate, or coaching. Each article has its own page, and the RSS feed carries published posts.
The complete CRE guide gives residential agents a place to begin with commercial terms, conversations, and next steps.