Private AI for Law Firms, Healthcare and Defense
Ask Your Own Documents a Question. Get an Answer That Cites the File.
Three questions. You get a straight answer on what it needs and what it costs.
Your organization already holds the answers. They are buried in matter files, patient records, drawings, claims history and twenty years of documents nobody can search properly. A private AI system lets people ask for them in plain language and get an answer that cites the exact file, running entirely on hardware you control. Nothing is uploaded, nothing is shared, nothing leaves.
What You Get
- Answers with receipts. Every response cites the document and section it came from, so checking it takes one click rather than a leap of faith.
- A model on your own hardware. Your building, your cloud account, or a machine with no internet connection. The documents never move.
- Search that understands the question. Keyword and meaning-based search run together and the rankings are fused, so a question phrased the way a person would phrase it still finds the right passage.
- Permissions that hold. Filters run before retrieval, so a document somebody is not entitled to see cannot appear in their answer.
- A system that gets measured. A written set of test questions, run on every change, including questions that must return nothing.
Built For
AI for Law Firms
Ask a question across every matter file you hold and get an answer that cites the document and the page.
Discovery, precedent, prior engagements and the memo somebody wrote four years ago, searchable in plain language instead of by filename. Nothing leaves the firm, so privileged material never touches a public model, and every answer points at the file it came from so an associate can verify it in one click rather than trusting it.
HIPAA Compliant AI for Healthcare Groups
Clinical and administrative records, answerable in plain language, inside your own environment.
Protected health information stays on infrastructure you already control and already audit, which means no new vendor on the paperwork and no data leaving the group to reach a model. Retrieval is filtered before the search runs, so a record a given user may not see cannot appear in that user’s answer.
Air Gapped AI for Defense and Aerospace Suppliers
A model and an index that work with no internet connection at all.
Drawings, specifications and program data stay on the network they already live on, including networks with nothing leaving them. The whole system installs and runs disconnected: model weights, index and interface all sit inside the boundary, and no call goes out to anybody’s API for an answer to come back.
Search Across Research Archives
Terabytes and upward, where finding the right dataset is its own job.
Indexing beside the data instead of copying it somewhere new, which is the same problem as running a catalog over petabytes and the work with the longest track record here. Researchers ask what exists and get pointed straight at it rather than hunting a share drive.
Utilities and Operational Records
Drawings, procedures and system records that stay inside the operational boundary.
The people with the questions are usually not the people who know where the answer is filed. A private system closes that gap without moving anything out of the environment it is meant to stay in.
Insurance Policy and Claims History
The useful answers are inside the legacy system, which is exactly the problem.
Work here usually starts with the policy or claims platform rather than with the model, because that is where the data is. Getting at it cleanly is a database and integration job first, which is the other half of what this shop does.
Why This Beats the Tool You Are Already Being Sold
Most AI products want your documents on their servers, answer without showing their sources, and cannot tell you why a given passage came back. That is fine for drafting an email and useless for work where being wrong is expensive. A private system inverts all three: the data stays put, every answer is traceable to a file, and what may be retrieved is a configuration you control rather than a promise in somebody’s contract. It also does not get more expensive every time you add a user.
Proof, Not Slides
Three systems built and run here do the parts this work is made of:
- A private knowledge platform indexing about two thousand internal documents, answering with the file and heading behind each passage, with superseded material filtered out before retrieval. Its written test set passed 21 of 21 questions at 181 ms average on its last run.
- A compliance platform whose AI connector exposes sixteen read-only tools and never returns a name, a date of birth, an ID number, an image or the contents of a document. The boundary is the design rather than a setting.
- Archive software whose connector reads and verifies by default, where anything that changes data sits behind a permission level that can only be raised in the desktop application, and which makes no network calls at all in its default mode.
All three are on the work page. See also local AI for how models and hardware are chosen, and AI search and retrieval for how the index is built.
Who Builds It
One senior developer, working since 2008, based in Scottsdale. The person who scopes the system writes the code, racks it, and answers the phone afterward. No account manager, no handoff to a junior, no offshore team. When the system holds a firm’s matter files or a supplier’s program data, knowing exactly who has been inside it is worth something.
Frequently Asked Questions
What does private AI actually mean?
The language model runs on hardware you control: a server in your building, your own cloud account, or a machine with no internet connection at all. Your documents are indexed where they already live. Questions and answers never leave that boundary. It is the opposite of pasting a document into a public chat window, and the technology is ordinary rather than exotic.
Can it run completely offline?
Yes. An air gapped install puts the model weights, the index and the interface inside the network and needs nothing from outside to answer a question. Updates are carried in deliberately rather than pulled down automatically. This is the normal shape for defense and aerospace work and it is no harder to support than a connected one.
How do you prove an answer is real and not invented?
Every answer cites the document and the section it came from, openable in one click. Retrieval is filtered before the search runs rather than after, so material that should not surface never reaches the model. And the system is measured against a written set of test questions, including questions that must return nothing, so quality is checked rather than assumed.
Will it work with the systems we already have?
That is usually most of the job. Documents sit in file shares, document management systems, SQL databases and line-of-business applications never designed to be searched this way. Getting at them cleanly is a database and integration problem, which is what this shop has done since 2008, and the AI layer goes on top once the data is reachable.
What hardware does it need?
Less than people expect. A single server with a modern GPU handles a document set for a department or a mid-sized firm, and the model is sized to the work rather than to a benchmark. The right specification comes out of what the documents are and how many people will use it, and you get that answer before anything is bought.
Do you work outside Arizona?
The install and the first conversations work best in person, so the Phoenix metro and Arizona are the easy case. Work further out runs through scheduled on-site visits with remote implementation between them. The system itself is the same wherever it is racked.
Put Your Own Documents to Work
Free quote, no obligation. Scottsdale-based since 2008.