Ask the workspaceGrounded in your documents
Ask anything across your documents…
Private AI for serious businesses
Private AI computers for businesses that cannot send their data to ChatGPT.
We build secure AI systems that help your team search, analyze, summarize, and draft across your own documents — without exposing confidential data to outside AI providers.
Book a private AI systems call
For accounting firms, law firms, clinics, construction businesses, family offices, and operational teams handling sensitive documents.
The problem
Most businesses want AI. They do not want their data sprayed across external tools.
Your team has valuable operational knowledge trapped in client folders, prior work product, internal SOPs, and email threads. Generic AI tools are useful, but they break down when the work requires privacy, permissions, document grounding, and firm-specific context.
Trapped in
- Client folders and prior work product
- Internal SOPs and checklists
- Email threads and correspondence
- PDFs, scans, and contracts
- Spreadsheets and bookkeeping exports
- Regulatory notices and filings
Eaten by manual work
- Searching folders for last year’s version
- Re-reading old documents to recall context
- Comparing versions across years and cases
- Drafting the same response over and over
- Checking what’s missing from a file
- Summarizing long documents by hand
Today: scattered, manual, repeatedOne private layer over the same documents
So staff keep doing the work manually. A private AI computer fixes that.
What you build
A private AI layer over your own documents and workflows.
Not a chatbot floating next to your business. An operational workspace built around the actual documents and workflows your team uses every day.
01
Search across internal documents
Plain-English queries over your full file system.
02
Ask questions about client, project, or matter files
Grounded in the documents themselves, with citations.
03
Summarize long PDFs, notices, reports, and correspondence
Hours of reading reduced to a structured brief.
04
Compare documents across years, versions, or cases
Year-over-year diffs flagged in seconds.
05
Identify what is missing from a file
Required fields, signatures, and supporting docs.
06
Draft client-ready responses
In your firm’s voice, queued for review and sign-off.
07
Generate internal memos and checklists
From SOPs, templates, and prior matters.
08
Automate repeatable operational tasks
Routine review work that does not need a human first pass.
You decide what the system can read. You decide what it can write. You decide what leaves your network.
Principles
How we work, in four lines.
These are the constraints we hold ourselves to. They are baked into the architecture of every deployment, not bolted on at the end.
The AI runs in your perimeter.
Models, documents, and chat live on hardware or in a dedicated tenancy you control. Your data is not used to train anyone else’s model.
Human approval on every write.
The system drafts, compares, summarizes, and routes. People sign off. Nothing leaves the firm under your name without an explicit human decision.
Every answer is grounded in a document.
Outputs cite the page, the file, the version. Where the model does not have a source, it says so rather than inventing one. Hallucinations are testable.
No vendor lock-in on the inference path.
The system runs on open-source models you can swap. If a vendor changes their terms next quarter, your operations do not.
Example deployment
Accounting firm AI computer
A regional accounting firm needed an internal AI system that could help staff analyze client files, answer tax and document questions, and automate routine review work — without sending confidential client data to outside AI providers.
Project size$48,000 build
ClientRegional accounting firm
Use caseTax, advisory, and client document workflows
We built a dedicated private AI environment for firm-wide document analysis and reasoning, scoped for a regional practice rather than a hyperscaler-scale deployment. Models, documents, and chat all run inside the firm’s perimeter.
Document types included
- Prior returns
- Engagement letters
- IRS and state notices
- Bookkeeping exports
- Payroll documents
- K-1s and 1099s
- Advisory memos
- Client correspondence
- Internal SOPs and templates
Core workflows
- Search client files in plain English
- Summarize prior filings on demand
- Compare year-over-year returns
- Review and triage tax notices
- Flag missing client information
- Answer document-specific questions
- Draft client-ready responses
- Pull the right SOP for the right matter
Security model
- Role-based access
- Document-level permissions
- IP and network restrictions
- Retention controls
- Audit logging
- Local document processing
- No third-party AI APIs
- Admin kill-switch
Have a similar workflow?
Book a call →
Reference deployment · regional accounting firm, ~40 staff
Who this is for
Built for businesses where privacy, context, and workflow matter.
This is a fit for teams that handle confidential operational data and repeat document-heavy workflows. The pattern transfers cleanly across regulated and unregulated industries.
Strong fits
- Accounting firms
- Law firms
- Medical practices and clinics
- Construction and homebuilding companies
- Family offices
- Consulting firms
- Financial advisory firms
- Research and technical teams
Common patterns
- High volume of confidential documents
- Repeat workflows with firm-specific judgment
- A staff bottleneck on repetitive reading and drafting
- A privacy or compliance posture that rules out public AI
- Specialty knowledge trapped in old files and senior heads
- Operational data spread across multiple systems
Especially good fit if you hear
- “We have thousands of documents, but nobody can find anything.”
- “Our staff keep asking the same operational questions.”
- “We need AI, but we cannot put this data into ChatGPT.”
- “Our workflows depend on old files, client history, and internal judgment.”
- “We want automation, but not at the cost of privacy or control.”
In their words
What partners and principals tell us.
“We had a stack of client files nobody could navigate and a staff hours away from burnout. Six weeks in, we are answering tax questions in seconds instead of half-days.”
“The compliance question went away once we saw the architecture. Everything stays in our perimeter. The audit trail is better than what we had before AI.”
“I did not want to be the firm that put client documents into ChatGPT. This is the version I can actually deploy.”
Deployment
Local AI infrastructure, sized for your business.
We do not start by forcing your company into a giant enterprise platform. We design a private AI environment sized to your actual workload, the number of staff who will use it, and the sensitivity of the documents involved.
Inputs
Client folders and prior work productInternal SOPs and checklistsEmail threads and correspondencePDFs, scans, and contractsSpreadsheets and bookkeeping exports
Your perimeterNothing leaves your network
Private AI computerLocal models · grounded retrieval · approval gate
Document indexLocal modelsPermissionsAudit log
Outputs
Search across internal documentsAsk questions about client, project, or matter filesSummarize long PDFs, notices, reports, and correspondenceCompare documents across years, versions, or casesIdentify what is missing from a file
Private GPU infrastructure
On-site, private cloud, or hybrid. You own the hardware or the dedicated tenancy.
Local document ingestion
Documents are processed and indexed inside your perimeter, not on an outside vendor’s servers.
Secure document search
Search and retrieval respect existing folder, role, and matter-level permissions.
Permission-aware AI access
The model can only see what the user asking is already allowed to see.
Workflow-specific tools
Drafting, comparing, and summarizing built around the actual jobs your team repeats.
Audit logs and admin controls
Every prompt, every retrieval, every output — visible to your administrators.
The goal is simple: give your team serious AI capability without turning your confidential business data into someone else’s training material or API payload.
What happens on the call
Book a 30-minute private AI systems call.
No generic AI pitch. No forced platform demo. We talk through your actual business workflows and where private AI does and does not make sense.
We will map
- Your highest-value document workflows
- Where your team currently loses time
- What data needs to stay private
- Which documents and systems matter most
- What should be automated first
- Whether a private AI computer makes financial sense for you
Book a private AI systems call
You leave with a rough architecture, a priority workflow map, and an honest view of whether this is worth building.
Project packages
Three ways to engage.
Most engagements fall into one of three shapes. Where you start depends on how clear the workflows are and how ready your team is to deploy.
Workflow audit
For unclear scope and leadership alignment
Two to four weeks. We map the highest-value workflows, the documents involved, and the privacy constraints. You leave with a buildable architecture, a priority list, and an honest go/no-go.
Private AI computer build
For firms ready to deploy internal AI for real work
A dedicated AI computer grounded in your documents, permissions, and operational workflows. Configured for the jobs your team actually repeats. Deployed inside your perimeter or in a private cloud tenancy.
Private AI infrastructure + automation
For sensitive data, repeatable workflows, and high staff leverage
A deeper build with private GPU infrastructure, custom workflows, integrations, audit logging, and ongoing support. For businesses where the AI layer becomes operational infrastructure, not an experiment.
Private AI computer builds commonly start in the tens of thousands depending on security requirements, document volume, workflows, integrations, and infrastructure needs.
Security
Built for confidential work.
Private AI is not just about model choice. It is about the whole operating environment. Every system we deploy is designed around these controls from day one.
01Data privacy — your documents stay in your perimeter.
02User and role-based permissions.
03Document-level access control.
04Auditability — every prompt and retrieval logged.
05IP and network restrictions.
06Retention rules — you control how long data persists.
07Local processing for the most sensitive workloads.
08Clear admin controls and kill-switches.
09Reduced dependency on external AI APIs.
10Optional air-gapped deployment.
For many businesses, the value is not just better AI. It is the ability to use AI on the documents they were previously unwilling to expose.
Who builds this
Founder-led. No account team.
We design hands-on and ship end-to-end. The people who design the system are the people who deploy it. The same rigor we brought to Fortune 100 environments, scaled to fit a partner-led firm.
BJ
Brian Joseff
Co-founder
Designs the architecture and the security model for every engagement. Background in private-cloud AI for regulated industries.
CS
Chandler Supple
Co-founder
Leads delivery and the on-site work with each firm. Background in operational AI for document- and process-heavy businesses.
FAQ
Common questions.
No. ChatGPT is a general-purpose AI tool. This is a private workspace grounded in your company’s own documents, permissions, and workflows. The model only sees what your team has chosen to expose, and outputs are tied back to the source documents they were drawn from.
Build a private AI computer for your business.
For teams with sensitive documents, repeatable workflows, and too much knowledge trapped in files.