Ziyad P.

Private AI in Your Own Cloud | HIPAA, Regulated Data, AWS & Azure

Your legal team already told you what the problem is. You cannot send patient records, transaction data or client files to someone else's API. So most AI vendors are out before the first meeting ends. That is the work I do. I put the models inside your own AWS or Azure account. Your infrastructure, your access controls, your audit logs so the data never leaves. I built payment infrastructure before this. 𝐈 𝐜𝐨-𝐟𝐨𝐮𝐧𝐝𝐞𝐝 𝐒𝐚𝐟𝐞𝐩𝐚𝐲, 𝐚 𝐘 𝐂𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐨𝐫 𝐛𝐚𝐜𝐤𝐞𝐝 𝐩𝐚𝐲𝐦𝐞𝐧𝐭 𝐠𝐚𝐭𝐞𝐰𝐚𝐲, 𝐚𝐧𝐝 𝐥𝐞𝐝 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐭𝐡𝐞𝐫𝐞. 𝐁𝐚𝐧𝐤 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬, 𝐜𝐚𝐫𝐝 𝐧𝐞𝐭𝐰𝐨𝐫𝐤𝐬, 𝐫𝐞𝐜𝐮𝐫𝐫𝐢𝐧𝐠 𝐛𝐢𝐥𝐥𝐢𝐧𝐠, 𝐥𝐢𝐯𝐞 𝐦𝐨𝐧𝐞𝐲 𝐦𝐨𝐯𝐢𝐧𝐠 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐡𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐞𝐯𝐞𝐫𝐲 𝐝𝐚𝐲. 𝐖𝐡𝐞𝐧 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐛𝐫𝐞𝐚𝐤𝐬 𝐢𝐧 𝐩𝐚𝐲𝐦𝐞𝐧𝐭𝐬, 𝐢𝐭 𝐢𝐬 𝐧𝐨𝐭 𝐚 𝐛𝐮𝐠 𝐫𝐞𝐩𝐨𝐫𝐭. 𝐈𝐭 𝐢𝐬 𝐚 𝐫𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫 𝐚𝐬𝐤𝐢𝐧𝐠 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬. That is the difference between someone who builds AI demos and someone who builds systems that hold up under review. Healthcare and finance look like different worlds. They are the same problem underneath. Protected data. A regulator with real power. Old systems nobody can replace. An integration layer nobody documented. HIPAA and PCI ask for different paperwork, but the architecture that satisfies either one is built the same way. 𝐖𝐡𝐨 𝐈 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 Clinics, outpatient groups and digital health platforms under HIPAA. Credit desks and trading operations. Payment companies and fintech infrastructure. Private equity firms handling deal and investor data. Managed service providers carrying regulated client books. If your AI decision needs a compliance officer in the room, you are the person I want to talk to. 𝐖𝐡𝐚𝐭 𝐈 𝐛𝐮𝐢𝐥𝐝 ● Private AI running inside your cloud, on AWS Bedrock, Azure OpenAI, or open weight models you host yourself. ● Search and answers across your own documents. Credit memos, contracts, clinical records, reports, whatever your team currently reads manually because it cannot go anywhere near a public model. ● EMR and EHR integration, patient intake and clinical workflow automation, built so PHI never crosses your boundary. ● Credit, trading and transaction data work. Retrieval across credit memos, counterparty filings and internal research, with entitlements that mirror your existing information barriers so a model cannot surface what a desk is not permitted to see. ● AI agents wired into the systems you already run. Your EHR, your ledger, your practice management software, your CRM. ● The messy integration work. HL7, FHIR, X12, ISO 20022, and every undocumented variation your organisation has picked up over twenty years. ● Access control, IAM design and audit logging built for the compliance review from day one, not bolted on after the auditor asks. 𝐇𝐨𝐰 𝐭𝐡𝐢𝐬 𝐮𝐬𝐮𝐚𝐥𝐥𝐲 𝐬𝐭𝐚𝐫𝐭𝐬 ● Not with a proposal. With a conversation about your constraint. Which regulator, which auditor, which data, which systems you already have. ● Then a paid architecture phase. You get a written design, a risk assessment, and a clear picture of what deployment actually involves. Something real to take to your board or your compliance team before you commit to anything larger. ● After that, one workflow built and running in your environment. Proof it works before we scale it. ● Most clients expand from there. That is the point of starting small. 𝐖𝐡𝐲 𝐦𝐞 Very few people build this properly. Plenty can call an API. Far fewer have shipped infrastructure where a compliance failure has real consequences, and that experience is the whole job here. I take a small number of clients at a time and work directly with whoever owns the decision. Cornell. Top Rated on Upwork. If your data cannot leave your environment, send me a message. That is exactly the conversation I want.