Private AI for sensitive data
AI agents and knowledge bases that run inside your own cloud. Your data never leaves your control.
Production AI agents and RAG on AWS, Microsoft Azure, or Google Cloud — for insurance, financial services, healthcare, legal, public safety, and more. We build for organizations where sending PHI, CJI, or client data to a third-party AI API is not an option.
18 years delivering software · ISO 27001:2022 certified · AWS Partner Network member
Your documents
policies · claims · records · contracts
AI agent + knowledge base
model runs in your account · not trained on your data
Answer with sources
This claim meets the coverage terms in section 3.2. Recommend approval — route to a handler for sign-off.
Public AI APIs stay outside the line. Prompts and documents never leave your environment.
18 yrs
Delivering software since 2008
Any cloud
AWS · Azure · Google Cloud
100%
Data stays in your account
ISO 27001
Certified information security
01The problem
Why organizations with sensitive data are stuck on AI
Teams want AI assistants over internal documents, policies, claims files, and case records — but compliance rightly blocks sending PHI, CJI, or client data to public AI APIs.
Generic chatbot vendors can't answer "where exactly does our data go?" Meanwhile competitors automate while the AI initiative sits in legal review.
01
Compliance blocks public APIs
Legal and security teams won't approve sending regulated data to third-party AI services with unclear data handling.
02
Vendors can't answer the audit question
Generic chatbot platforms can't document data flows, retention, or model training in terms your auditors accept.
03
PoCs don't survive production scrutiny
Demo chatbots work until accuracy, citations, guardrails, and integration requirements show up in the real environment.
04
Competitors move while you wait
Every quarter in legal review is a quarter your competitors deploy private AI inside their own infrastructure.
02The architecture
Built for audit scrutiny.
We build on the cloud you already trust — Amazon Bedrock on AWS, Azure OpenAI Service on Microsoft Azure, or Vertex AI on Google Cloud — or run open-source models in your own environment. Prompts and data are not used to train models and stay in your own account and region.
Everything is yours: your cloud account, your code, your IP.
- Private networking — no public internet transit
- Encryption at rest with your own keys, TLS in transit
- Role-based access and full audit logging
- Guardrails for content filtering and PII handling
- Model choice: Claude, GPT, Gemini, Llama, Mistral, and more — benchmarked to your requirements
- Full code and infrastructure-as-code transfer — no vendor lock-in
03Use cases
What we build
Internal Knowledge Assistants
RAG over policies, SOPs, contracts, clinical guidelines, or case files — with citations to source documents so answers are verifiable, not guessed.
Claims & Document Automation Agents
Intake, classification, extraction, and adjudication support — agents that handle document-heavy workflows without sending data to public AI APIs.
Learn more →Compliance & Audit Copilots
Query regulations and your own audit trails in plain language. Built for teams that need traceable answers, not black-box responses.
Case & Records Intelligence for Public Safety
Search and summarization built to CJIS Security Policy requirements, over case records and operational documents — inside your secured environment.
Legacy System + AI Integration
18 years of enterprise Java/.NET fluency connected to modern AI agents. Bridge legacy knowledge to RAG and agents without a full rewrite.
Learn more →04How we build it
From architecture to production
- 01
1–2 weeks
Architecture & Compliance Design
Data-flow mapping, security review with your compliance team, and written success metrics before any build begins.
- 02
4–6 weeks
Working Pilot on Your Data
Functioning agent or knowledge base inside your own cloud account over a real document set — with testable accuracy evaluation.
- 03
8–14 weeks
Production Hardening
EHR, claims, or case-management integrations; guardrails; evaluation pipelines; monitoring; load and security testing.
- 04
Ongoing
Operate & Improve
Monthly model and prompt updates, accuracy monitoring, new sources, quarterly reviews — via our Managed AI Operations retainer.
05Engagement
How an engagement works
1–2 weeks
Discovery & Architecture Review
Free 30-min assessment, then fixed-fee architecture engagement
4–6 weeks
Pilot
Working agent or knowledge base on your data inside your own cloud account
Learn more →8–14 weeks
Production
Integrations, guardrails, evaluation pipelines, monitoring, security hardening
Monthly
AI Operations Retainer
Accuracy monitoring, model updates, new sources, quarterly reviews
Learn more →Every engagement is scoped to your situation.
Scope depends on document volume, integration complexity, compliance frameworks, and model requirements. We provide a written proposal after the architecture review — no generic quotes.
06FAQ
Questions we hear most
Ready to deploy private AI inside your own cloud?
Book a free 30-minute assessment. We'll map your data flows, compliance constraints, and the fastest path to a production agent or knowledge base — inside your own environment. No obligation. Senior architect on every call.