Private AI for firms that cannot send client data to public tools.
AI is genuinely useful for document-heavy work. It is also a confidentiality problem when the documents in question belong to your clients. Archway Point helps businesses in St. Charles County and the greater St. Louis area deploy AI that runs on infrastructure they control.
The problem with pasting client files into a chat box
Staff at professional firms are already using AI tools, usually without telling anyone. They paste in a contract to get a summary, or a client's financials to draft a memo. It saves them an hour and creates a disclosure question nobody at the firm has answered.
Banning the tools does not work; people simply stop mentioning it. The workable answer is to give them something equally useful that runs inside your own walls, so the productivity gain arrives without the confidentiality exposure.
Start with a feasibility assessment
Private AI is oversold, and a great deal of what gets pitched is not worth the hardware. We start with a short assessment: what documents you have and in what formats, what questions people actually want answered, what your confidentiality obligations require, and what hardware would be needed to serve the number of people who would use it.
You get a written recommendation with real numbers. That recommendation is sometimes "this is not worth doing yet," and we would rather tell you that in week one than after you have bought a server.
What we build
- Local document search. Ask a question in plain language and get an answer drawn from your own files, with citations back to the source document. This is the single highest-value starting point for most firms.
- Internal AI assistants. A private assistant that knows your templates, policies, and past work, for drafting and summarizing without anything leaving your network.
- Internal knowledge bases. Turning years of accumulated documents, procedures, and institutional memory into something a new hire can query on day one.
- Secure workflow automation. Intake processing, document classification, and routine extraction handled locally.
- GPU server planning and builds. Sizing, procurement, configuration, and the ongoing support to keep it running.
- Hybrid strategy. A documented boundary defining what may be processed externally and what must stay local, so the policy is explicit rather than assumed.
Infrastructure you own rather than rent forever
Per-seat AI subscriptions are a permanent operating cost that rises with headcount. A dedicated machine is a capital purchase you control, depreciate, and can repurpose. For firms with steady, predictable use the economics often favor ownership within two to three years, and you keep the hardware at the end of it.
That machine is also useful beyond AI. Dedicated compute can host internal applications, handle heavy processing jobs, and serve as capacity for whatever comes next.
Typical engagements
Most firms should start small and expand once the value is proven.
| Engagement | What it covers | Typical timeline |
|---|---|---|
| Feasibility assessment | Document and use-case review, confidentiality requirements, hardware sizing, written recommendation with costs | 1–2 weeks |
| Local document search | Document indexing, private search and question answering with source citations, user access controls | 3–6 weeks |
| Private AI assistant | Internal assistant tuned to your templates and procedures, with logging, access control, and staff training | 4–10 weeks |
| GPU server build | Hardware sizing and procurement, configuration, model deployment, monitoring, and documentation | 3–8 weeks |
| AI usage policy | A written policy defining what staff may use, what must stay internal, and how to demonstrate compliance to clients | 1–2 weeks |
| Ongoing support | Model updates, index refreshes, hardware monitoring, capacity planning, and user support | Ongoing, monthly |
Common questions
Is this just ChatGPT with a different label?
No. A private deployment runs open-weight models on hardware you control, either in your office or on a dedicated server rented in your name. Your documents are indexed locally, queries are answered locally, and nothing is transmitted to a third-party AI provider or used to train anyone's model. The experience is similar; the data path is fundamentally different.
Does any of our data leave the building?
In an on-premises deployment, no. That is the entire point. Documents stay on your storage, the model runs on your hardware, and the system can be operated with no outbound internet access at all if your requirements demand it. In a hybrid design, we draw an explicit line about what may leave and what may not, and we document it so you can show a client or auditor exactly where the boundary sits.
What hardware does this actually require?
Less than most people assume. A document search and question-answering system for a small firm typically runs comfortably on a single workstation-class GPU. Larger models and heavier concurrent use push you toward a dedicated GPU server. We size the hardware to your document volume and how many people will use it at once, and we will tell you if your use case does not justify the spend.
What does a private AI project cost?
The assessment is a fixed, modest engagement that tells you whether the idea is worth pursuing at all. If it is, cost divides into hardware, which you own, and setup, which is a one-time project. Many firms start with a single GPU workstation in the low four figures rather than a rack of equipment. We would rather scope you a small system that proves value than a large one that sits underused.
Are open models good enough compared to the big cloud services?
For the tasks most professional firms actually want, yes. Searching your own documents, summarizing a long file, drafting from templates, and answering questions about internal material are all well within reach of current open-weight models. If your use case genuinely needs frontier-model reasoning, we will say so and help you design a hybrid approach where sensitive material stays local and only non-sensitive work goes out.
Who is this genuinely a good fit for?
Firms with a confidentiality obligation and a document problem. Law practices with case files, accounting firms with client records, medical and dental practices with patient information, insurance agencies with policy documents, and consultancies under client NDAs. If your data is not sensitive and your document volume is small, a commercial cloud tool is probably the better and cheaper answer, and we will tell you that.
Related services
See whether private AI makes sense for your firm.
Tell us what documents you work with and what you wish you could ask them. We will tell you honestly whether a private deployment is worth it, and what it would take.