Application
Where does the user-facing application or service run?
Private AI guide
Confidential Document AI
The privacy question is not simply whether the interface runs locally. A confidential-document workflow can involve document storage, parsing, embeddings, retrieval, prompts and inference across different locations.
Decision context
Start with the boundary, not the brand.
Sensitive documents, client files, internal records and other material that must remain inside a defined privacy boundary.
Documents & indexes
Where are source files, extracted text, embeddings and indexes stored?
Inference
Where are prompts and retrieved passages processed by the model?
Hardware
Can the selected model, context and runtime fit the available RAM or VRAM?
Common failure modes
What not to assume
Treating “self-hosted” as proof that nothing leaves the machine.
Ignoring embeddings, indexes or document-processing components when defining the data boundary.
Selecting a model before checking runtime, quantization, context and memory requirements.
Hard constraints win: mandatory privacy, platform, hardware or license requirements exclude incompatible configurations instead of merely lowering their rank.
Verified baselines
Reference configurations related to this guide
These configurations are evidence-backed launch references. They illustrate different privacy and hardware trade-offs; they are not universal recommendations.
Consultant — Confidential Documents — RTX 4070
AnythingLLM + Ollama + Gemma 3 12B IT (QAT/int4 profile)
GOODfully-local
Legal — Confidential Documents — Apple 64 GB
PrivateGPT + llama.cpp + Mistral Small 3.1 24B
EXCELLENTfully-local
Finance — Document Analysis — RTX 4070 Ti SUPER
AnythingLLM + Ollama + Qwen3-14B
GOODfully-local
Professional context
Apply the architecture to your work
Continue the decision
Use the evidence-backed catalog
FAQ
Key questions
Can confidential document AI be fully local?
It can be, but only when the complete configuration keeps the application, document processing, storage and inference inside the required boundary. STACKELIER evaluates the configuration rather than assuming this from a product name.
Is local document storage enough?
No. Documents may remain local while prompts, retrieved passages or inference requests are still sent to an external service.
What happens when a privacy requirement is mandatory?
It becomes a hard constraint. A reference stack that violates the required boundary is excluded rather than merely receiving a lower score.