Not every practice is a good candidate for private AI. Some are excellent candidates and don't know it. Some assume they are and aren't quite there yet. The signals that indicate fit are specific enough to identify without a lengthy evaluation — and being clear about them up front is more useful than a sales conversation that ends in a mismatch.

Here are the four signals that matter, and an honest account of when the answer is not yet.

The document signal

The right question is not how many documents a practice has in total. It is how many documents staff need to find things in on a regular basis. A firm with 50,000 archived files but a staff that rarely references them is a different situation than a practice with 3,000 active case files that everyone searches daily.

A meaningful starting threshold is somewhere around 500 documents that staff currently search or reference regularly. Below that, the time saved rarely justifies the investment. Above it, the return compounds quickly — every query that previously required a manual search through a filing system now returns cited passages in seconds.

The document types that indicate strongest fit: case files, contracts, prior matters, patient charts, clinical protocols, SOPs, batch records, deviation reports, policy documents, research memos, audit records. These are dense, reference-heavy, and typically the hardest to search quickly. They are exactly what retrieval-augmented AI handles well.

The confidentiality signal

The most important qualifying question is whether uploading the practice's documents to a cloud AI service would require a disclosure analysis, a Business Associate Agreement, or an explanation to a client.

For law firms, the analysis turns on attorney-client privilege and whether transmission to a third party constitutes a disclosure that could affect it. For medical practices, it turns on HIPAA and whether a BAA is in place and adequate. For accounting firms and estate practices, it turns on client confidentiality obligations and engagement letter terms. For life sciences and manufacturing companies, it turns on trade secret exposure and validated systems integrity.

If the honest answer to that question is "we are not sure," private deployment removes it entirely. The architecture itself is the answer.

Practices that handle none of these document types — whose work is primarily administrative, whose documents contain no privileged or regulated information — may find cloud AI fully adequate. Private deployment is not for every use case. It is specifically for the use cases where sending documents outside the building creates a problem worth solving.

The staff signal

V1 is designed for one to twenty users. The starting point with the strongest return is two to eight people who regularly search the same document set. Below that threshold, the time savings are real but the cost-per-user math is harder to justify. Above twenty, the engagement requires a different hardware tier and a longer validation phase.

Distributed staff or remote access needs change the network design but do not disqualify a practice. A firm with staff across two offices or attorneys who work from home can still be served — the architecture is different from a single-office deployment, and the scope reflects that. The discovery conversation is where that gets worked out.

One staff consideration that matters more than headcount: is there someone in the practice willing to act as a designated administrator? This is not a technical role. It means being the point of contact for adding documents to the system, managing user accounts, and reaching out when something needs attention. A practice with no one available for even that minimal function is not operationally ready, regardless of document volume.

The infrastructure signal

The infrastructure requirements for V1 are deliberately low. A reliable office network, a space for a small dedicated computer, and a standard 120V outlet. No server room. No existing IT infrastructure beyond what any functioning office already has. The system connects to the office network via Ethernet and is accessible from any device on that network through a web browser.

Existing practice management software, electronic health records, billing systems, and document storage are unaffected. V1 does not connect to any of these systems. It reads from its own indexed document set and operates independently of whatever else is running in the office.

The one infrastructure consideration worth raising early: network reliability. A practice whose office internet connection is frequently interrupted should know that V1 continues operating during outages — it does not require internet connectivity to function — but the initial configuration and any remote support do require a network connection.

When the answer is not yet

A practice that is in active transition — a merger, an EHR migration, a planned office move, a significant change in document management practice — is usually better served by waiting for stable state before adding infrastructure. The deployment will go better and the system will be configured against the actual environment rather than the transitional one.

A practice whose primary AI need is something V1 does not do — email integration, calendar access, client portal connectivity, or external data feeds — is not a fit for V1. Those are real capabilities; they are not what this engagement delivers. If that is the primary use case, the honest answer is that a different solution or a future version of this one is the right path.

And a practice that is simply not ready to make a hardware commitment — even a small one — on a new technology category is not ready yet. That is a reasonable position. The discovery conversation does not require a commitment. It requires curiosity and a willingness to spend sixty minutes looking at whether the fit is real.

The decision frame

A practice that has meaningful document volume, handles confidential information that should not leave the building, has two or more staff who search those documents regularly, and has stable operations is a strong candidate. The discovery conversation produces a clear answer: fit, not yet, or not the right solution. All three are useful outcomes.