LOW/CODE

Healthcare builds move faster when you scope privacy rules early. We confirm where HIPAA applies, review third-party and AI tools that touch patient data, and plan audit trails to match policy. Karoline, our project manager, explains it in a video. DM us.

The maternal health platform we built had a specific launch timeline. Weeks one and two: compliance assessment, six tools evaluated, two confirmed. Weeks four to eight: development. Week eleven: Google approval. Under two weeks from submission.

The compliance environment a healthcare product operates in shapes the first two weeks. Private clinic: a design conversation. Government program: a compliance review. Health tech startup: mapping regulatory scope for future scale.

Six tools evaluated during refinement, before any architecture decision was made. Two signed a BAA without restrictions. The architecture was built around those two from the start.

Where does the compliance risk actually live in your architecture? Which tools will sign a BAA for your specific use case? Both answered before any integration is selected.

We built no-code apps for years. Now, almost everything we build is custom code. What changed? AI flipped the economics of software development. In our latest podcast episode, we break down why we made the switch and what it means for your business. 🔗 lowc.dev/TVrcq

We mapped which formulas encode regulatory requirements, which edge cases need a dedicated code path, and which outputs require a human decision before the build started. The product worked correctly from the first close cycle.

Financial product validation tests the full range of inputs, including the edge cases. An error that runs undetected for six months costs more than the same error caught before launch. That cost scales with how wrong the calculation is and how long it runs.

We built a financial reporting product by starting with the billing logic. The contract structures the client assumed were too specific to mention were the ones that mattered most.

More AI agents ≠ better automation. What matters is context, clear responsibilities, and human oversight. In our latest podcast episode, we break down why 2 well-designed agents can outperform 30 poorly managed ones. 🔗 Hear the full episode: lowc.dev/IjtCR

Building real estate software means navigating MLS data agreements, Fair Housing guidelines, and evidentiary standards inside every transaction. Wallace, our project manager, brings direct domain expertise from working with brokers to ensure your application operates securely.

Real estate workflow automation operates inside a compliance framework. Qualification flows. Listing filters. Communication sequences. Fair Housing governs all three.

MLS data comes with a licensing agreement that governs every field. What can be cached, how long it can be stored, and what requires attribution when displayed. At LOW/CODE Agency, the integration is built to what the license permits.

The most sensitive data in real estate moves through the product before the transaction begins. Social Security numbers. Income docs. Buyer financials. Earnest money details. All of it defined in the retention policy before the schema is designed.

Four questions before the first wireframe. MLS permissions. Fair Housing constraints. Digital signature requirements. Data retention limits. Those answers shape everything that follows.

AI adoption is widening the performance gap between companies in the same economy. In our latest podcast episode, we explore how deeper AI use is changing confidence, hiring, productivity, resilience, and why relying on one AI provider can be risky. 🔗 lowc.dev/qRCPk

Software is becoming front-end agnostic 🔍 Our sister brand, Phos AI Labs, got hands-on and built 2 agentic features into our internal dashboard: AI that queries company data by role and a Claude routine that automatically fixes simple bugs. 🔗 youtu.be/emdPiT4e6gY

Our sister brand, Phos AI Labs, audited our operations, and we dive into the results in our latest podcast episode. More importantly, we unpack WHY the best AI starts with understanding the business first. 🔗 Hear the full episode: lowc.dev/EsEuv

#AI won’t fix a broken operation if it can’t run itself first. In this episode, we unpack how GAF cut training admin time by 75% by building the automation foundation before adding AI. Hear the full episode: lowc.dev/SSqZr

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