Ahmad K.

I help healthcare 🏥, legal ⚖️, construction 🚧, operations-heavy businesses, and B2B SaaS teams implement AI-powered systems that fit into real-world workflows, and keep them reliable when patients, lawyers, and auditors depend on them.

Here's what I've learned after 10+ years shipping production software: the model is rarely the problem. What breaks is everything around it. Messy data goes in, nobody checks what comes out, and there's no audit trail when something goes wrong. That's the part I build.

👨‍💻How I work:
▪ Before we start, you get a written plan. What we're building, what could break, and the milestones. No guessing what you're getting.
▪ A short demo video every week, so you see real progress, not just "it's going well."
▪ I work your hours. Afternoons through midnight here, so your morning messages don't wait till tomorrow.
▪ Code lives in your repo and runs on your accounts. You own everything from day one.
▪ 30 days of free bug fixes after launch, plus docs and a handover call.

Some of what I've shipped:

• A healthcare AI platform for a multi-location clinic group. It has voice and web booking, an AI scribe that drafts SOAP notes, and two-way EHR sync over FHIR R4. PHI is stripped before anything touches an LLM. No-shows dropped sharply and doctors got hours of charting back.
• Serva Legal. A process-serving firm went from spreadsheets and a giant script to a multi-tenant SaaS. AI reads incoming messages and PDFs, a human verifies, and affidavits that took most of an hour now generate almost instantly.
• GolfPay360. It double-booked tee times under heavy traffic. I rebuilt it with proper locking and idempotent retries, and the double-bookings stopped.
• Shoreline Waste. Route planning and live fleet tracking with photo-verified pickups. It cut fuel costs and mostly ended customer disputes.
• AI Deal Analyzer for real estate. You put an address in and get a full deal analysis out. Python does all the math and Claude only explains it, so the numbers never get made up.

💻 Demos of these are in my portfolio.

How I build so it holds up:
• Sensitive data de-identified before any LLM call
• A human signs off on any AI output that touches a regulated record
• AI handles messy input like notifications, PDFs and calls. Plain code handles math and rules
• Tenant isolation and append-only audit logs enforced in the database, not just the app
• Every AI call logged, costed and traceable, with fallbacks when the model fails