Confidential legal document analysis
Analyze matter documents with explicit control over where files, extracted text and model inference are processed.
Private AI by profession
Private AI for Lawyers
Legal AI decisions need more than a product name. STACKELIER separates application, data and inference locations, then applies hard privacy and hardware constraints before a reference stack can qualify.
Workflows
Private AI workflows for Lawyers & Legal
The useful configuration depends on what the work actually contains, not only on the profession label.
Private RAG over matter files
Retrieve from a matter-specific corpus while keeping ingestion, indexes, retrieved passages and inference inside the required boundary.
Local knowledge retrieval
Use controlled internal knowledge sources without treating a self-hosted front end as proof that the full retrieval path is private.
Decision priorities
What changes the recommendation
Matter confidentiality
Use this as a constraint when comparing reference stacks and when completing the Finder.
Controlled document and index storage
Use this as a constraint when comparing reference stacks and when completing the Finder.
Evidence for deployment and model terms
Use this as a constraint when comparing reference stacks and when completing the Finder.
Privacy boundary
Self-hosted is not enough for Lawyers & Legal.
Confidentiality requirements can exclude otherwise capable configurations. STACKELIER treats mandatory privacy requirements as hard constraints rather than ranking penalties.
Application
Where does the interface or service run?
Data
Where are documents, embeddings and indexes stored?
Inference
Where does the model actually process the request?
Evidence
Which authoritative sources support those claims?
Verified baseline
Reference stacks for Lawyers & Legal
These are verified launch configurations already used by the deterministic Finder. They are reference profiles, not universal recommendations.
Legal — Confidential Documents — Apple 64 GB
PrivateGPT + llama.cpp + Mistral Small 3.1 24B
EXCELLENTfully-local
Legal — Private RAG — RTX 4090
Open WebUI + Ollama + Mistral Small 3.1 24B
EXCELLENTself-hosted
Legal — Private RAG — RTX 4070 Ti SUPER
Open WebUI + Ollama + Qwen3-14B
GOOD_WITH_BOUNDED_CONTEXTfully-local
Legal — Private PDF Q&A — Apple 32 GB
Open WebUI + Ollama + Qwen3-8B
GOODfully-local
Hard constraints win: if a mandatory privacy or hardware requirement is incompatible, the stack is excluded rather than merely ranked lower.
Next decision
Move from research to a configuration.
Use the guides for the architecture questions, compare verified baselines side by side, or run the Finder with your exact privacy and hardware constraints.
Confidential Document AI
Understand the decision boundary before choosing a specific stack.
Self-hosted RAG
Understand the decision boundary before choosing a specific stack.
Compare verified Lawyers & Legal stacks
Open the relevant reference configurations side by side.
Run the Stack Finder
Apply your exact task, privacy requirement and hardware instead of relying on a generic baseline.
FAQ
Questions about private AI for Lawyers & Legal
What makes an AI stack suitable for confidential legal work?
The configuration must satisfy the required application, data and inference boundary and have enough evidence to support those claims. Hardware and model terms are evaluated separately from privacy.
Does a self-hosted legal AI interface guarantee confidentiality?
No. The interface may be self-hosted while embeddings, storage or inference still use an external service. STACKELIER evaluates the complete path.
How should legal teams compare private RAG options?
Compare ingestion, storage, retrieval, inference, hardware fit and evidence together. If a mandatory privacy condition is violated, the configuration should be excluded rather than merely ranked lower.