Private AI infrastructure
Private AI. Installed where your data lives.
BrooksFlow designs, deploys, and manages secure AI systems for organizations that need private document search, local model deployment, workflow automation, and controlled access to sensitive business knowledge.
The problem
Useful AI and sensitive data are pulling in opposite directions.
Your team already knows AI saves hours. So either they paste sensitive work into public tools you can't see — or you block AI entirely and watch the hours stay lost. Both options are bad. The fix isn't a policy memo; it's infrastructure.
Shadow AI is already happening
Client files, patient-adjacent documents, and internal knowledge end up in public chatbots because they're the easiest tool available.
Blocking AI just moves the cost
Bans don't remove the demand — they push it to personal devices and keep your team slower than competitors who solved this.
Enterprise platforms assume a platform team
Private AI offerings built for the Fortune 500 assume in-house infrastructure, security, and ML staff you don't have.
What BrooksFlow builds
ChatGPT-style tools — without sending sensitive work outside the business.
One system, four capabilities. Installed on infrastructure you control, behind a network boundary you can point to.
Private AI chat
Approved users get a controlled interface for internal questions, drafting, and workflow support — with named access and admin logging.
Private document search
RAG over approved documents with citations, evaluation checks, and explicit uncertainty. Answers point back to your own files.
Workflow automation
Intake routing, summarization, drafting, and exception review over internal systems — on customer-controlled infrastructure.
Managed local stack
Ubuntu, Docker, model serving, vector search, storage, logging, access, backups, and support — operated so your team doesn't have to.
Model
local / open-weight
Search
private RAG
Access
VPN + roles
The install
Real hardware. A real boundary. Not another subscription.
Your models run on machines inside your walls or in infrastructure you control. Your documents are indexed locally. Access happens over a private network with named users. If you unplug it, it's off — that's the point.
0
business days to a roadmap
2–0 wks
to a working pilot
0
days of included support
0
data egress by default
Core offerings
Start with a roadmap. Prove one workflow. Then operate it.
Pricing is shown as planning ranges — every engagement is quoted against its actual scope. Hardware and third-party costs are paid separately by the customer.
How engagement works
Assess, pilot, deploy, operate.
Assess
A readiness assessment maps workflows, data sensitivity, infrastructure, and risk — and may recommend a pilot, a smaller automation, a cloud approach, or no AI build at all.
Pilot
One contained workflow with real users, defined data boundaries, and measurable success criteria. Hardware is scoped and paid separately.
Deploy
Proven workflows expand to departments or the whole organization, with infrastructure sized and procured to match.
Operate
Managed AI Ops keeps the system monitored, patched, tuned, documented, and adopted — on a cadence that fits your risk profile.
Security posture
Controls before scale.
Every workflow gets a documented boundary before it reaches users: what data is in scope, where it runs, who can touch it, and where humans stay accountable.
Review the security approach01
Data minimization
Only the data the approved workflow needs. Narrow scope beats broad access.
02
Private network access
Tailscale or WireGuard, internal-only model and data services, named users.
03
Human accountability
Qualified people stay responsible for legal, clinical, and financial decisions.
04
Documented operation
Data flows, access assumptions, review steps, and ownership on paper.
Pricing ranges
Planning ranges, not one-size-fits-all packages.
Final pricing depends on scope, data sensitivity, deployment model, hardware, integrations, and operating cadence.
Readiness Assessment
Typically $7,500–$15,000
Private AI Pilot
Typically $35,000–$95,000+ · plus customer-paid hardware
Department Deployment
Typically $100,000–$250,000+ · plus customer-paid hardware
Private AI Room / Rack
Custom · typically $250,000+ program
Managed AI Ops
Typically $3,500–$25,000+/month
Hardware, GPUs, servers, racks, storage, networking, power, cooling, shipping, taxes, third-party software, and facility work are paid separately by the customer — approved and paid upfront before procurement. BrooksFlow does not finance customer hardware. Full pricing details
FAQ
The questions everyone asks first.
AI models, document search, and workflow tools running on infrastructure you control — your own hardware, your own network, or your own cloud account — instead of a public AI service. Your documents are indexed where they live, and access is limited to named users on a private network.
Not always. Some workflows run well on CPU or a single workstation-class GPU. The assessment sizes hardware to the actual workload — not every deployment requires a full rack or a large GPU server.
The customer, directly and upfront. Hardware, GPUs, servers, racks, storage, networking, power, cooling, shipping, taxes, third-party software, and facility work are paid separately by the customer. BrooksFlow can recommend, configure, and manage procurement, but does not finance customer hardware.
Yes. On-premise deployment — models, document index, and access all inside your walls — is the core pattern BrooksFlow builds for organizations with strict data control requirements.
Yes. For teams that prefer cloud, workflows can be deployed inside your own cloud account, identity model, and logging environment — customer-controlled rather than vendor-hosted.
Next step
Start with one sensitive workflow and a clear data boundary.
Bring the workflow, the documents involved, and the reason public AI tools make you uncomfortable. BrooksFlow will help decide whether an assessment, pilot, or managed ops path fits.