Private AI by profession

Private AI for Consultants

Use AI on client work without treating “self-hosted” as a privacy guarantee. STACKELIER checks where the application runs, where data is stored, where inference happens, and whether the hardware can realistically run the configuration.

Find my stack →How STACKELIER scores fit

Workflows

Private AI workflows for Consultants

The useful configuration depends on what the work actually contains, not only on the profession label.

Use case

Confidential document analysis

Review client reports, proposals, contracts or research material while keeping the required document and inference boundary explicit.

Use case

Private RAG over client files

Search across a controlled client corpus without assuming that a local chat interface also keeps embeddings, retrieval and inference local.

Use case

Private meeting transcription

Transcribe sensitive calls with a configuration whose audio processing, model execution and retained artifacts fit the engagement boundary.

Decision priorities

What changes the recommendation

Priority 01

NDA and client-confidentiality boundaries

Use this as a constraint when comparing reference stacks and when completing the Finder.

Priority 02

Document and retrieval data location

Use this as a constraint when comparing reference stacks and when completing the Finder.

Priority 03

Portable hardware for client-facing work

Use this as a constraint when comparing reference stacks and when completing the Finder.

Privacy boundary

Self-hosted is not enough for Consultants.

For NDA-bound or sensitive engagements, the privacy boundary must be evaluated at configuration level. A local interface connected to an external inference API is not equivalent to a fully local stack.

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

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.

Related guide

Confidential Document AI

Understand the decision boundary before choosing a specific stack.

Related guide

Self-hosted RAG

Understand the decision boundary before choosing a specific stack.

Compare

Compare verified Consultants stacks

Open the relevant reference configurations side by side.

Personalize

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 Consultants

Can consultants use private AI for client documents?

Yes, when the complete configuration satisfies the engagement’s privacy requirements. STACKELIER separates application, document/data and inference locations instead of assuming privacy from the product name.

When is private RAG useful for consultants?

Private RAG is useful when work depends on retrieving from a controlled collection of client or project material. The ingestion, index, retrieval and inference path still needs to fit the required privacy boundary.

What hardware should a consultant check first?

Start with the hardware already available, then evaluate model, runtime, quantization, context and memory overhead together. A model that does not fit the working machine is not a viable recommendation.