Since January, we've been building new projects with AI coding tools, which are programs that help developers write and test software. We found that building this way is more efficient for our team, and the code we write belongs to you and lives in your own accounts, so you can host it wherever you choose. AI coding tools speed up a lot of the work, and our developers still design the structure of the software, check the security, and test the results with your team, right? Sometimes a prebuilt tool covers what you need, and I'll tell you that on the call. At @LowCodeAgency, we figure out the right approach for each custom software project during discovery. I'm happy to talk it through when you DM me "AI build."

Healthcare compliance is three different problems depending on who you are building for. Private clinic: flexibility above the HIPAA floor. Government-regulated program: state-approved forms, exact language, the product conforms to the regulatory artifact. Health tech startup: evaluated at the scale the product is designed to reach, with minimal flexibility from day one. Most agencies apply one approach to all three. The diagnosis changes depending on the patient.

AI agent vs automation, explained with one example. Automation follows a rule. A new row shows up in a spreadsheet, and an email goes out. The rule runs the same way every time, and it's typically zeros or ones. An AI agent takes a goal and picks its own next step. It fits work that changes from case to case, like researching a prospect, where each step depends on what the agent found before it. If your steps repeat the same way every time, build an automation, because it costs less and runs more reliably. Which process in your company has steps that change every time? Happy to help you figure out which one fits.

Picture a request for a second iPad landing in an approver's inbox. A thoughtful reply takes a few minutes to write, so the answer often waits for a quieter day. In the issuance app we built for a nonprofit client, admins review requests item by item. Standard requests get approved in one click, and a mixed request gets a yes for the iPhone and a decline for the second iPad in the same pass. Every decline needs a written reason, and the app emails it to the requester right away. The requester gets clarity the same day, and the approver gets a documented answer with no extra email to write. If you review a handful of requests a month, email works fine. Once requests flow across departments, DM me and I will walk you through it.

Ask this before building a custom CRM: does it tell my team who to call first? Ours had 1,007 companies and almost no data on them. Reps googled every account before a call, or called whoever replied fastest. An off-the-shelf CRM only holds what people type in. So we built our own qualification into it. An AI agent researches each company: headcount, revenue range, AI signals. One rule scores the fit. Every field shows its source. Reps now open a deal already knowing who to call first.

Someone on your team pastes a customer's details into a free AI app. Where does it go? Depends on the plan: does it train on your chats, how long is it kept, what contract covers it. For customer data: business plan, signed data agreement, training off.

Five minutes after a sales call ends, a draft follow-up is already in my inbox. The agent pulled the transcript, scored the deal, built the to-do list, matched the case studies to the conversation, and wrote the email. All of it on its own. I review it and decide what to send. That's where my judgment matters. My team used to spend two to three hours a day on follow-up. Now they open the action center, review what the agent prepared, and send. We built this for ourselves first. You need to live with it before you hand it to anyone else.

An AI audit gives you a bunch of ideas, and the next step is choosing. Once the list of ideas is ready, each one needs an estimate of value and an estimate of effort. Ideas with high value and manageable effort go first. Ideas that save a small amount of time per year usually fit a simple tool, and when a tool already covers one, we point you to it. Companies bring in an advisor to decide where development hours make sense before anyone writes code. The thing is, a lot of the value comes from knowing what to skip. List the processes that take the most time from your team and rank them by value and effort. We're happy to do the ranking with you at @LowCodeAgency.

AI projects give better results when the software underneath them is ready to support them. A business owner I talked with described an AI project that worked well in one area and needed more support in another. The part that read documents and filled in fields worked with good accuracy, and the part that connected to daily work needed software built for that kind of connection. He decided to update his core software first. If you're planning an AI project, here is a short way to check your readiness. First, list the systems the AI needs to read from and write to. Second, check whether each system offers an API, which is a standard way for programs to exchange data. Third, decide which systems to update before the AI work begins. Sometimes a prebuilt AI tool works with your current software as it is, and I'll tell you when that's the case, right? At @LowCodeAgency, we map your current systems during discovery, the planning phase before the build. I'm happy to talk it through when you DM me "readiness."

I mean, landlords and plumbers are a terrible ICP, but this is right and that's why I built @phosailabs

Rising Ground. Fleet operations across New York. All of it tracked in Excel before we built their system. Jordan's words after launch: "What used to be spreadsheets now feels cleaner, more accountable, and easier to manage." One platform. Third year of continuous development.

HIPAA requires a complete record of every interaction with Protected Health Information, retained and accessible for six years. Most teams treat this as a storage question. It is an architecture question. If audit logging is not built into the schema from the start, retrofitting it means rebuilding the data layer after the fact. In healthcare products with communication infrastructure, the audit log responsibility does not automatically sit with the product team. It depends on how the Business Associate chain is structured. That conversation belongs in refinement, before any code is written.

AI agent vs chatbot comes down to one question, which is who picks the next step. A chatbot is smart and answers questions, and its work ends when the conversation ends. An AI agent takes a goal, picks the steps to reach it, uses tools, and checks its own work. We built one for an employer of record (a company that hires people abroad for foreign businesses). The goal is to find US companies about to hire in Mexico and write the first email to each one. The agent searches job posts, finds the hiring manager, and picks the closest case study. The full version runs about 90 steps, and the agent chooses the order. Start with the goal, right? The goal tells you whether a chatbot, an automation, or an agent fits. Happy to show it on a screen share if you're curious.

Hiring one person touches three departments: HR for payroll and benefits, IT for the laptop and phone, Facilities for the key card. Each team speaks its own language, so managers translated every request three times. For Rising Ground, a New York nonprofit we work with, we built one request form. The manager picks the laptop, phone, software, and building access in one place, and the app sends each item to the right team. It works like a solid foundation, and every floor above it gets easier to build, right? If your checklist lives in three inboxes, DM me and I will walk you through it.