A private AI workspace that runs on hardware inside your building. Start with secure chat, cited answers from your documents, and drafting. Expand into role-based workflows, overnight agents, and morning briefings when the use case calls for them.
Sentinel Labs ran the Q3 audit. The contract renews on March 1, 2027, with a 60-day notice window.
One surface inside the command center
Chat and document answers on your hardware
Secure chat, cited document answers, and drafting run locally on hardware you own.
Add workflows and agents when they earn their place
Add role-based workflows and scheduled agents after the core workspace proves its value.
Data leaves your network
Outbound traffic is blocked at the firewall, so there is no route for data to leave.
Patches, upgrades, and monitoring available
Select can handle model upgrades, security patches, monitoring, and scheduled maintenance.
A secure workspace on hardware you own, with private chat, document-aware answers, drafting tools, individual accounts, and cited sources your team can verify.
Local
Data
Cited
Answers
0
Egress
This private workspace is the core deployment. Role-based workflows, scheduled agents, and morning briefings are optional modules scoped after the core use case proves its value.
Inside the command center
Powered by always-on, locked-down agents
Optional expansion
Once the private workspace proves useful, we can add the operations web app your business runs on: role-based views, authentication, and the workflows each team needs. Scheduled agents work with defined rules and access levels, then prepare briefings, exceptions, and next actions for human review.
While the office sleeps
Always-on agents on your hardware
When people log in
Role-based command center
Role-based ops app with private AI underneath
Morning briefing
Overnight agents prepared 4 items
Exception
Contract queue
3 contracts past review deadline
Draft ready
Client letter
Follow-up from yesterday’s call
KPI delta
Pipeline summary
Up 12% vs last week
Source cited
Compliance check
Policy handbook §4.2
Always-on on-prem agents
Doc watcher
Re-index new and changed files
Access: All indexed files
Exception scan
Flag overdue contracts
Access: Ops role only
Draft assistant
Prepare follow-up drafts
Access: Sales templates
Why on-premise, why now
Two years ago, local models were too weak to trust with real work. That has changed. A single well-specified server can now handle document Q&A, drafting, and summaries for an entire office. You give up the raw scale of frontier cloud models, and in return you gain three things that matter: full control of your data, predictable cost with no per-token fees, and independence from any vendor policy changes.
Where Does Your Data Go?
Cloud AI
Your data leaves the building
Your documents
contracts, records, client files
The internet
out through your firewall
Vendor servers
third-party retention and policies
A third party processes and may retain your data
On-Premise AI
Everything stays inside your office
Your documents
on your own file share
Your AI server
local models on hardware you own
Your team
cited answers over your network
Outbound internet blocked at the firewall, nothing leaves
The privacy promise only holds if the network enforces it. The AI server lives on its own segment with general outbound internet blocked at the firewall. Office access uses TLS and individual accounts. Disks are encrypted at rest, and backups stay on your equipment. There is no persistent public endpoint. Any maintenance channel is client-authorized, time-limited, logged, and disabled outside approved windows.
01
Isolated segment
The AI server lives on its own network segment inside your office.
02
No outbound path
Internet egress is blocked at the firewall, so nothing can phone home.
03
Encrypted access
TLS in transit, individual accounts, disks encrypted at rest on your gear.
Isolated by Architecture, Not by Policy
Your office boundary
Office network
your team, ai.yourcompany.internal
Isolated segment
AI server
local models, chat, document index
How Your Documents Become Cited Answers
Always running
Your file share
Word, PDF, spreadsheets, scans
Document watcher
re-indexes new and changed files
Search index
passages embedded for retrieval
When you ask
Your question
asked in the secure chat
Local LLM
retrieves passages, composes the answer
Cited answer
sources linked for verification
A document watcher keeps an index current as files change. When someone asks a question, the local model retrieves the relevant passages, composes an answer, and cites the sources so your team can open them and verify. Designed for assistive work with a human in the loop, not unsupervised legal or medical decisions.
Is this right for you
The businesses that get the most from an on-premise command center share a profile: a real body of documents, recurring questions about them, and data that cannot leave the building. Your answers to these questions drive every technical decision.
What are three tasks you would do with this system next week? Which would save the most time or create the most value?
Where does it live today: file server, SharePoint, email, a line-of-business application, paper? What formats and roughly how much? How often does it change?
How many people will use it, and how many at the same time? Who should see HR documents, contracts, and financials? Role design shapes the architecture early.
What happens if an answer is wrong? We design for a simple rule: the system always shows sources and a human verifies. Where is extra review required?
Will the system handle protected health information or other regulated data? What audit-trail and chat-history retention rules do you need?
What does working look like three months after launch? We aim at one measurable outcome, agreed up front.
Which AI path
Private on-premise is the path when data cannot leave the building. Strategy and cloud product AI remain available when those fit better.
Roadmaps, readiness, and hands-on training so your team adopts agentic tools and modern stacks with confidence.
ExploreEmbed cloud LLMs, RAG, and automation into your products with guardrails, observability, and production UX.
ExploreA private AI workspace on hardware inside your office, with optional command-center workflows when the core use case is proven.
Current pathCommercial stages
These commercial stages explain what you authorize and purchase. The five-step delivery process below shows how we execute the work after each decision.
Prove the use case on your sample data before any capital spend, and size the system correctly.
Procure, secure, and install a dedicated server you own, then bring the command center live inside your network.
Ongoing maintenance so you get ownership of the system without needing an internal AI ops team.
Technology
Platform capabilities
Security & operations
How We Work
Discovery is step one. Build and deploy cover steps two through four. Operate begins at step five. No hardware is purchased until the discovery prototype has proven the use case.
We interview your team, gather sample documents, and build a working prototype you can watch answer real questions from your data. No hardware is purchased until the use case is proven.
We specify and procure a dedicated AI server sized to your team, then assemble, secure, and load-test it at our office. You own the hardware outright.
On-site installation, network isolation, and internal DNS (ai.yourcompany.internal), with verification that the system is unreachable from the internet.
We load your documents, tune answer quality, set up accounts and permissions, and train your team, including how to verify answers against their cited sources.
A check-in after a few weeks of real use, then ongoing managed maintenance: security patches, model upgrades, monitoring, and secure remote administration with your written sign-off.
Questions
Your production documents, prompts, embeddings, model inference, chat history, logs, and backups stay on your hardware inside your network. General outbound internet access is blocked at the firewall. Approved maintenance uses a separate client-controlled channel that is disabled outside scheduled windows.
The architecture can support a HIPAA-compliant deployment through safeguards such as role-based access, audit logging, encryption, local processing, and a Business Associate Agreement. Compliance also depends on your administrative, physical, and operational controls, which we review with your privacy and compliance stakeholders. The same technical controls can support legal, financial, and NDA-bound data, subject to each organization’s requirements.
No. The first phase is deliberately low-risk: we prove the use case with a working prototype before any hardware is purchased, which also lets us size the server correctly.
We can handle model upgrades, security patches, monitoring, and scheduled maintenance as a managed service. Your team still owns network approvals, identity and access decisions, facilities, compliance policy, and acceptable-use governance.
The system always shows its sources and a person verifies. Every answer cites the documents it drew from, so your team can open them and confirm. We tune retrieval quality during onboarding and design extra review into any high-stakes outputs. The platform is built for assistive work, not unsupervised legal or medical decisions.
The best fit is a real body of documents, recurring questions about them, and data that cannot leave the building: law firms, medical practices, financial advisors, and agencies under NDA are common examples. If that sounds like you, an introductory meeting is the fastest way to find out.
A dedicated tower or rack-class server sized after discovery for your concurrent users, document volume, and model needs. Power, noise, and footprint vary with that sizing. We specify the machine only after the prototype proves the use case, so you do not overbuy or underbuy.
The server has no persistent public endpoint, and general outbound access stays blocked. Remote maintenance uses a client-controlled channel that is authorized for a scheduled window, time-limited, logged, and disabled again when the work is complete. The exact mechanism is agreed during security design.
Frontier cloud models usually lead on broad reasoning and elastic scale, while local models provide stronger data control and predictable infrastructure cost. Both approaches can use private retrieval. Discovery benchmarks representative tasks against your quality, latency, privacy, and cost requirements before we recommend on-premise, cloud, or a hybrid design.
File shares are the common starting point, with a watcher that re-indexes new and changed files. SharePoint, document management systems, email archives, and other sources are scoped during discovery based on where your knowledge actually lives.
Backups stay on your own equipment, not a third-party cloud bucket. We configure on-site backups as part of deploy and operate. Exact recovery time depends on your hardware and backup schedule, which we define with you rather than promising a generic RTO.
You own the hardware and the data. We provide a documented hand-off so your team or another partner can keep the system running. Stopping managed ops does not revoke ownership of the box or the knowledge on it.
Go deeper
Our announcement covers how an engagement works, the security architecture, and the questions we work through with you, in more detail.
A private command center for your office
The first step is a short introductory meeting. We walk through your use cases and data, then a discovery phase produces a working prototype before you spend a dollar on hardware.
Book an Intro Meeting