Bring AI to your data — not your data to the AI.
We design and build private AI systems that run entirely inside your organization's AWS account. Your documents, your records, your users — the model works with all of it without any of it leaving your control.
Why public AI tools don't fit
Your data leaves the building
Every prompt to a public chatbot sends your content to someone else's servers. For regulated or sensitive data, that alone is disqualifying.
Generic models don't know your world
Public AI wasn't built on your policies, your programs, your case history. It approximates. You need answers grounded in your own material.
Enterprise AI is priced for enterprises
Per-seat subscriptions assume heavy, uniform usage. Most teams pay for far more than they use.
One team, from intake to answer
We're not a model vendor bolting a chatbot onto your stack. We build the whole system — the apps your users touch, the pipeline that moves and protects your data, and the AI layer on top — and we build it in your cloud.
Apps & admin console
Web and native iOS / Android apps for the people who submit information — plus an admin console where your staff manage records, run deterministic reports, reset user access, and trigger AWS actions like push notifications, without ever opening the AWS console.
Ingestion & backend
APIs and event-driven pipelines that pull from your existing sources — databases, document stores, S3, registries — into a governed data lake.
Privacy layer
PII is pseudonymized at the point of ingest, so downstream users and the model work against de-identified data by default.
AI layer
Retrieval-augmented generation on Amazon Bedrock: a chatbot for your team and an MCP endpoint for programmatic access, both answering only from your indexed content, with citations — and, where it fits, agent workflows that take actions in your systems.
How a private AI platform fits together
Backends vary by client. The privacy boundary and the in-account model don't.
Intake
iOS app
Android app
Websites
Security
Web application firewall
guards public sites and APIs
Application
Admin portal
records, reports, AWS operations — no console
Backend services
vary by client
Privacy
PII pseudonymization
identifiers stripped at ingest
Data lake
De-identified store
documents, records, databases
AI
Amazon Bedrock
embeddings + RAG index
Chatbot
cited answers
MCP server
programmatic access
A private AI platform for a national patient-advocacy nonprofit
The organization holds sensitive constituent records and a large body of program and research material. Staff and outside researchers needed to query it in plain language; none of it could be exposed to a third-party AI service. The engagement began as a paid security review and grew into the full build.
We built intake apps and APIs feeding a governed data lake, pseudonymization at ingest, a retrieval-augmented assistant on Amazon Bedrock, and an admin portal with role-based access and board-level reporting — all inside the organization's own AWS account.
Zero PII exposure by design
Identifiers are stripped at ingest; researchers query clinical data without ever touching personal information.
~90% lower infrastructure cost
Versus a comparable managed setup, through a serverless, pay-for-use architecture — extending a constrained nonprofit budget.
Multi-region disaster recovery
The full stack is reproduced from infrastructure-as-code and kept in daily sync across regions.
Who it's for
Built for organizations on AWS that have sensitive data and no in-house AI team.
Have data you can't put into a public AI tool?
Tell us what you're working with and what you need it to answer. We'll tell you what a private build would take.
Get in touch