Layer 01
Data
Reading, OCR, layout, classification, splitting, structured extraction, normalization and validation. The output is a record another system can consume, with confidence attached and nulls where the document is silent.
Private Document Intelligence
Private AI for documents and internal knowledge.
From documents to data, knowledge and action. Privately. Private Document Intelligence is the use of AI inside infrastructure the organization controls to understand, extract, search, transform and act on the information held in its documents, without handing that information to an external AI provider.
From documents to data, knowledge and action.
Most tools in this space stop after the first step. The value compounds across all three, because a field you extracted is only useful if something can act on it, and an answer is only trustworthy if you can see what it came from.
Layer 02
Knowledge
Indexing, lexical and semantic retrieval, comparison and grounded answers. The output is an answer with the passage behind it, and an honest refusal when the corpus does not support one.
Layer 03
Action
Conversion, redaction, PDF operations, archival formats, human review and approval, and tools an agent can call. The output is a changed artifact you can open and verify.
The documents worth automating are the ones you cannot send.
Contracts, claims, medical and legal records, personnel files, engineering drawings, correspondence and scanned archives. The repetitive document work that consumes the most staff time tends to sit precisely where an external AI service is blocked by policy, by regulation, by a customer contract, or by procurement.
Control
Residency you can point at
Files, indexes, embeddings and inference sit on machines you name. That supports a data-residency or governance requirement, which is a different and more honest claim than saying a product makes you compliant.
Cost
Volume stops being the variable
Document work is high volume by nature. Per-token pricing turns a back-office process into a bill that scales with the thing you were trying to automate. Owned compute does not.
Continuity
Works when the link does not
Air-gapped sites, field deployments, regulated networks and plain outages. A process that stops when an external API is unreachable was never really automated.
Fully local, or private with selective disclosure.
Both keep the source material inside your infrastructure. They differ in which model does the reasoning, and that difference has to be stated precisely rather than blurred into a single privacy claim.
Mode A
Fully local
A local model does the reasoning as well as the processing. Documents, OCR, indexes, embeddings and generated output all remain inside the perimeter, and no content reaches an external model. This is the mode for environments where that is a hard requirement.
Mode B
Private processing, external reasoning
An external AI assistant provides the conversational interface while the heavy, sensitive work happens locally through MCP tools. The original files, OCR, indexes and rendering never leave. What does leave is the result of the tool you allowed, so administrators control the disclosure rather than the assistant deciding for itself.
Worth stating plainly: in Mode B, anything a tool returns is disclosed to the external model. The defensible claim is that the source documents stay local and administrators decide which derived results are shared, not that nothing leaves the network.
What each one optimizes for.
These are all reasonable choices for the problem they were built for. The useful question is not which is better, but which constraint you are actually up against.
Compared to
Intelligent document processing
Optimizes for extraction accuracy at scale on known document types, usually as a managed cloud service with a template or training step. Strong where volumes are high and the document set is stable. Private document AI trades some of that specialization for local execution, general document handling and the knowledge and action layers.
Compared to
Cloud document AI services
Optimizes for immediate capability with no infrastructure and continuous model improvement. The tradeoff is that your documents are processed by a third party, and cost tracks volume. The distinction is where the file is opened, not what the model can do.
Compared to
RAG frameworks and toolkits
Optimizes for flexibility, giving engineers primitives to assemble a retrieval pipeline. Excellent for building something bespoke. The difference is assembly: a framework hands you components, where this category is expected to arrive working, with document handling, retrieval and citations already joined up.
Compared to
Private AI infrastructure
Optimizes for running models on your hardware: serving, scheduling, hardware acceleration and model lifecycle. That is the foundation rather than the outcome. Local inference is a prerequisite here, not the product. The work is what happens to the document once a model is available.
A layer, not a system of record.
Content management, records management, retention and access control belong to the systems that already own your content. This category understands, structures, retrieves and acts on that content, then hands the result to the applications, workflows and assistants that consume it. It is designed to sit alongside those systems rather than to become one.
See it working