Private AI

Private AI without building an AI research lab

Organizations increasingly want the benefits of AI while keeping sensitive data out of public AI platforms. That does not mean racks of GPUs or a team of machine-learning researchers. Many useful workloads run on surprisingly modest infrastructure.

What does "private AI" actually mean?

Private AI does not describe one specific architecture, and treating it as though it does is how projects get overbuilt.

For one organization it means every model and database operates entirely inside its own network. For another it means keeping sensitive documents on-premises while public information is processed elsewhere. A third may be perfectly comfortable using dedicated infrastructure managed by a trusted vendor but not a public generative AI service.

All three are private AI. They just have different boundaries.

Our first job is helping you define where yours are.

You don't need the biggest model

There is a strong tendency to evaluate AI systems almost entirely by benchmark scores and parameter counts. Real business workloads are different.

If a smaller model performs a task accurately, consistently, privately and inexpensively, it is the better tool — and it may be the only version of the project that fits inside your building.

And sometimes a language model is not the right tool at all. We use AI where it provides value and traditional software where traditional software works better.

Three shapes it takes

The same requirement, three different builds

These are illustrations rather than packages. The point is that "keep our data private" resolves into very different systems depending on what you actually need.

Everything stays inside

Models, indexes, extracted text and job queues all run on hardware the organization owns. Nothing crosses the network boundary, including at build time.

Fits: legal and personnel records, proprietary source code, anything covered by an internal policy that says "not off-site."

Private, but not yours to run

Processing happens on dedicated infrastructure we operate, with data handling, retention and access defined as part of the engagement.

Fits: organizations that want private processing without hiring anyone to maintain specialized AI infrastructure.

Split by sensitivity

Sensitive material never leaves; genuinely public material uses whatever is fastest and cheapest. The architecture enforces which is which.

Fits: almost every organization that handles both public records and confidential ones — which is most of them.

worker internal-doc-search nothing leaves the building

extract text PDF, Office, scans Runs on your network
generate embeddings local model Runs on your network
build index vector + full text Runs on your network
answer questions local language model Runs on your network

Runs on your network Runs inside your own environment.
Runs on managed Runs on infrastructure we operate for you.
Runs on cloud Uses a commercial cloud or AI service.

What we do

Private AI consulting and implementation

Most engagements start with a discovery conversation about the work rather than the technology, and end with a system somebody actually uses. In between:

Workload evaluation — what is worth automating, and what is not
Privacy boundaries — what may be processed where, written down
AI architecture and pipeline design
Infrastructure planning and GPU sizing
Local model deployment and evaluation
Integration with the systems you already run
Workflow development and normalization of outputs
Monitoring, updates and ongoing management

Projects can be one-off implementations or ongoing engagements. Several organizations need the first, discover the second, and that is a fine way to arrive at it.

Where to next