AI agency. OpenAI Select Partner & Anthropic Partner. 400+ systems built. Coca-Cola, Amex, Zapier, Sotheby's, Medtronic.
Miami, FL
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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.
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Real estate workflow automation operates inside a compliance framework.
Qualification flows. Listing filters. Communication sequences.
Fair Housing governs all three.
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Most RAG systems can retrieve information.
Far fewer can survive a financial services compliance review.
In finance, RAG has to handle audit trails, restricted data, regulatory updates and complex documents from day one.
Here’s what separates production RAG from a demo 🧵
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Our latest blog post compares 6 RAG development companies working in financial services.
The key differentiator isn’t “can they build RAG?”
It’s whether they can build it for regulated finance.
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If you’re planning a financial services RAG project, don’t start with model selection.
Start with compliance architecture, document structure, permissions, integrations and re-indexing.
Read the full guide + company breakdown here 👇
lowc.dev/YaytT
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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.
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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.
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Four questions before the first wireframe.
MLS permissions. Fair Housing constraints. Digital signature requirements. Data retention limits.
Those answers shape everything that follows.
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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
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Most B2B companies treat product documentation like a support resource.
That’s a mistake.
Your docs can:
→ Convert technical buyers
→ Reduce support tickets
→ Shorten sales cycles
→ Rank on Google
Here’s how to build them properly 🧵
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The biggest documentation mistakes?
❌ Launching docs after the product
❌ No search
❌ Feature-first navigation
❌ No changelog
❌ Docs hidden from the main site
❌ No clear owner
Documentation gets outdated fast when nobody owns it.
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Great B2B documentation does 3 jobs at once:
💰 Helps technical buyers evaluate
🎧 Reduces support load
🔎 Creates searchable acquisition pages
Treat docs like part of the product—not a footer link.
Read the full guide here 👇
lowc.dev/HmMry
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