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Custom AI Agents AI Knowledge Assistant Custom Software & Apps Private AI Infrastructure All services →Private AI infrastructure from Seqora puts your AI systems on cloud accounts you own, on AWS, Vercel, Supabase or the tenancy you already run, instead of on a vendor's shared platform. It's for practices, firms and finance teams whose data is patient records, case files or client financials. We set up encryption, secrets, role-based access, audit logs and monitoring as code, and configure every model provider so your data is never used to train public models. You keep the keys, the logs and the bill.
Private AI infrastructure is the cloud setup that runs your AI systems inside accounts you own, with data encrypted, access limited by role, every action logged, and model providers bound by terms that keep your inputs out of training. It lets a practice or firm use AI on patient, case or financial data without handing that data to a vendor's shared platform.
Most AI tools are someone else's software on someone else's servers. Paste in a patient chart, a client's case file or a bank statement, and it now sits in a vendor's database, under their retention rules and their staff's access.
Some consumer AI apps can also use what you type to improve their models unless someone changes a setting. Business APIs generally exclude your data from training, but only if someone chooses them, configures them and reads the terms.
Then there's the part that's easy to miss: logs. An error message that prints a patient's name, or a debug file holding a full document, exposes that data even when the database itself is locked down.
Owning the accounts is the control. When the system runs in your cloud, you can see every resource, revoke any key and switch it all off without asking us.
About how long it took to get a client's production stack live on its own AWS, Vercel and MongoDB Atlas accounts
Groovit · 2026
Automated backend checks on a client's platform by its October release, up from 116
Groovit · Oct 2026
Each piece is defined in your accounts and written down, so your IT lead or compliance reviewer can inspect it without taking our word for it.
AWS, Vercel, Supabase, Google Cloud or the tenancy you already run, defined in Terraform or OpenTofu. You own the accounts and the bill, and the whole setup can be reviewed line by line and rebuilt from the code.
TLS on every connection, encrypted databases and file storage at rest, and a setup built so API keys live in a managed store such as AWS Secrets Manager or KMS-encrypted Parameter Store rather than in the code.
Each person and service gets only the access its job needs. Staff sign in with their own accounts, an admin manages the team list, and data can be separated per location, client or case team.
A record of who viewed, changed or approved what, kept apart from the app logs. Logs and alerts carry record IDs instead of names or document text, and credentials are redacted before anything is written.
Model calls go through business APIs whose terms exclude your data from training, with a provider that signs a BAA when patient data is involved. For data that can't leave your network, an open-weights model on your own hardware.
Alarms that email a person, deploys that roll back on failed health checks, and a tested backup restore. Retention and deletion rules are written down per data type, so you know how long each record is kept and how to remove it, including from an AI knowledge assistant.
The infrastructure usually goes up alongside the first AI system it hosts, so these steps run in parallel with that build.
We list every system the AI will touch, what kind of data each one holds, who needs access, and what your industry's rules require. You get a data-flow diagram your compliance reviewer can sign off on before anything is built.
We open or tighten cloud accounts in your company's name, define the infrastructure as code, and set up the secrets store, encryption, roles and network rules. If patient data is involved, the BAA is signed before any of it moves.
We choose model providers by data type, confirm their terms exclude training, and route steps that handle patient records or other regulated data to a BAA-covered provider or a model on your own hardware. Prompts and outputs that contain sensitive data are kept out of the logs.
We check every role's access, trigger the alarms, restore a backup and pull log samples to confirm no patient or client details leak into them. Deploys get automatic rollback on failed health checks.
You get the infrastructure code, an architecture diagram, an access list and runbooks for rollback, restores and access changes. We can stay on for monitoring, or your team takes it from there.
Three production systems. Digital Ink runs on secured AWS infrastructure, 100 Boulevard's AI validation step runs inside the client's existing cloud system, and Groovit is a platform rebuild on the client's own cloud accounts that shows the same infrastructure discipline.
Digital Ink reads handwritten exam notes and diagram markings and drafts reports at a Los Angeles dental trauma practice. It runs on secured AWS infrastructure built for HIPAA workloads, with an admin dashboard and an audit log, and nothing goes out until the doctor and staff have reviewed it.
Running at the practice on secured AWS infrastructure, and the practice has since referred us to another healthcare client.
Our AI validation step runs inside 100 Boulevard's existing cloud system rather than on ours. It reads members' statements, pay stubs, benefit letters and tax forms where they already live, checks each amount and writes a plain-language reason for anything it can't verify.
Zero extraction errors across the first 30 production cases, and 576 cases completed in a single production day.
Not an AI build, but the same infrastructure work: Groovit's production stack rebuilt on the client's own AWS, Vercel and MongoDB Atlas accounts, defined as code with automatic rollback, plus per-school data isolation. Movement detection runs in the browser, so no student video leaves the device.
The camera game went from about 10 to about 50 frames per second on a classroom Chromebook, and 53 defects were closed in the first three and a half weeks.
Groovit's production stack went live on the client's own cloud accounts about one week after work began. If you stop working with us, you remove our access and the system keeps running.
We don't hold a security certification and don't claim one. We sign a BAA when the work touches patient data and give your reviewer the architecture, access list and log samples to check for themselves.
When documents shouldn't go to any outside AI provider, we can run an open-weights model on hardware you control instead, so no file reaches an external model provider.
| Compare | Off-the-shelf AI tool | Seqora private AI infrastructure |
|---|---|---|
| Where it runs | The vendor's shared cloud | Cloud accounts in your company's name |
| Who holds the keys | The vendor | You, in your own secrets store |
| Training on your data | Depends on the plan and the settings | Excluded by business API terms, or a local model when needed |
| Audit trail | Whatever the vendor chooses to show you | Your own logs of who did what and when |
| Patient data | BAA depends on the vendor and plan | BAA signed before any patient data moves |
| Leaving | Export what they allow, then lose access | Remove our access; the system keeps running |
Clinical notes, treatment plans and insurance paperwork processed in your accounts, with a BAA signed first and no patient details written to logs.
DentalMedical records, bills and client messages processed inside the firm's own tenancy, with an audit trail of who opened what and access split by case team.
PI lawW-9s, formation documents, EIN letters and loan agreements collected through password-protected, deal-specific upload links and saved to the firm's own OneDrive, the pattern behind the onboarding portal we built for AST Defeasance.
Infrastructure is usually scoped with the system it hosts, as a one-time setup plus optional monitoring. Cloud and model usage is billed to you directly by the providers. These are the things that move the number:
What we don't claim: any security certification, or that any system is breach-proof. We commit to a setup you can inspect, logs your reviewer can read, and a BAA before patient data moves.
No. We only connect model providers through business APIs whose terms exclude your inputs and outputs from training, and we check those terms for each provider before anything is wired up. For data that shouldn't reach any outside provider, we can run an open-weights model on your own hardware instead. Consumer chat apps are never part of the system.
No, and we don't claim to be. There is no official government HIPAA certification, and we don't hold a third-party security certification. What we do is build for HIPAA workloads, sign a BAA when the work touches patient data, and give your compliance reviewer the architecture, access list and log samples to check.
You do. The accounts are opened in your company's name and billed to you, and we work through access you grant and can revoke at any time. At handover you get the infrastructure code, the application code, an architecture diagram and runbooks. If you stop working with us, you remove our access and the system keeps running.
Usually a few weeks, built alongside the first AI system it hosts. Groovit's production stack was live on the client's own accounts about one week after work began, though no patient data or BAA was involved there. Deployments that connect several systems or need a BAA-covered model provider take longer. You get a dated plan after the data map.
Yes. We deploy into existing AWS, Google Cloud, Vercel or Supabase accounts, or next to a system you already run. For 100 Boulevard, our AI validation step runs inside their existing cloud system alongside their team's existing process. When you ask us to set up or harden the accounts, we review what's there and tighten access before adding anything. An AI document processing pipeline can read files where they already live instead of copying them elsewhere.
You find out fast and can trace it. Alarms email a person when something fails, deploys roll back on failed health checks, and audit logs show who touched what and when. The setup is built so keys live in a secrets store and an exposed one can be rotated without changing code. Logs are kept free of patient and client details, so they don't widen an incident.
Further reading: AI agent, workflow, or dashboard: how to decide where AI actually belongs
Book a free call. We'll map which AI tools touch your data now, where that data ends up, and what it would take to move the work into accounts you own.