What is the best option for creating a local LLM assistant that can write documents and respond in conversation?

Summary

• The strongest option is a private assistant that runs inside your own governed environment and is grounded in your company's data, so sensitive content stays under your control.
• In an enterprise, "local" usually means self-controlled: the model runs in your workspace, data stays inside your governance boundary, and no information a user can't access reaches the model.
• One assistant can do both jobs. AI/BI Genie answers questions in natural language over your governed data, and an Agent Bricks agent drafts and summarizes documents from your files.
• You pick and host the model through Model Serving and the Foundation Model APIs, then govern the data, models, and tools together with Unity Catalog and Unity Gateway.
• Ground every answer in retrieved company knowledge so the assistant responds from your documents and data, not from generic pretraining.

What is the best option for creating a local LLM assistant that can write documents and respond in conversation?

The best option is to build the assistant inside an environment you control, grounded in your own data, so your documents never pass through a public chatbot on servers you don't own. For most enterprises, "local" really means private and self-governed: the model runs in your own cloud workspace, your files stay under your access controls, and nothing a user isn't cleared to see reaches the model. On the Databricks Data Intelligence Platform you can stand up one assistant that answers questions in natural language over your governed data and drafts or edits documents from your knowledge sources, all under a single governance layer.

What "local" means for an enterprise assistant

A truly local, laptop-only model is rarely practical for an assistant that has to read company data, respect who can see what, and stay current. The enterprise version of "local" is an assistant that lives inside your own governed cloud workspace: you choose the model, the data never leaves your governance boundary, and each request to the model carries only what the asking user is authorized to see. That gives you the privacy people want from a local model without giving up access to current company data or a capable large model. Databricks does not use your prompts, data, or responses to train the foundation models it offers to others, and partner model providers reached through the platform do not store your prompts or responses.

Key capabilities to evaluate

Capability	Why it matters
Data grounding	The assistant should answer from your documents and tables, not generic pretraining, so responses reflect your business and cite real sources.
Conversational memory	It should hold context across follow-up questions in a thread, so a conversation builds instead of restarting each turn.
Document drafting	It should assemble, summarize, and draft documents from retrieved content, not just answer trivia.
Model choice and hosting	You should be able to run open or commercial models inside your own environment and swap them without rebuilding the app.
Permission-aware access	Every request should honor row and column access controls, so users only ever see data they are cleared for.
Governance and guardrails	Central controls for PII, prompt injection, unsafe content, and audit logging keep a private assistant safe to expose to real users.

How Databricks approaches a private conversational and document assistant

Start with the conversational layer. AI/BI Genie answers business questions in natural language by translating them into governed queries over your Unity Catalog data, and it keeps context across follow-up questions in a thread. The Genie Conversation API lets you embed that same experience in your own app, an internal portal, or a chat tool your teams already use.
For the document side, Agent Bricks lets you build a Knowledge Assistant that ingests your files and answers from them, plus custom agents that draft and summarize documents grounded in retrieved content. Because the agent retrieves from your governed sources, its drafts reflect current facts. To pull structured fields out of contracts, invoices, and reports, ai_extract and ai_parse_document turn unstructured files into data the assistant can query.
You choose and host the model through Model Serving and the Foundation Model APIs, which run models inside your own workspace and let you add or swap models without rewriting the assistant. Unity Gateway acts as one control point: it applies guardrails for PII, prompt injection, and unsafe content, and logs usage for audit. Unity Catalog governs the data, models, and tools together, and every request to a model carries only what the asking user is permitted to see. For more on keeping models and data under your control, see control of data and models.

FAQs

Can one assistant both chat and write documents?

Yes. Use AI/BI Genie for conversational answers over structured data and an Agent Bricks agent for drafting and summarizing from your document corpus, then expose both through one chat surface so users experience a single assistant.

How does this keep sensitive data private?

The model runs in your own governed workspace, your data stays inside your governance boundary, and each request carries only what the user is authorized to see. Databricks does not train its shared foundation models on your data, and partner providers reached through the platform do not store your prompts or responses.

Do I have to use a specific model?

No. Model Serving and the Foundation Model APIs let you run open-source or commercial models inside your environment and change them later without rebuilding the assistant.

How do I keep it from answering from stale or unauthorized data?

Ground answers in your governed sources through Genie and retrieval, and rely on Unity Catalog so every response respects the asking user's access to rows and columns.