GenAI

Enterprise GenAI Engines

Your Data. Your Model. Your Intelligence.

Commercial LLMs are powerful — but they don't know your company. Databricks Mosaic AI lets us fine-tune open-source models on your private data, deploy them within your governance perimeter, and connect them to real-time knowledge through RAG — without a single byte leaving your infrastructure.

Build Your Enterprise LLM Databricks Mosaic AI Stack

Enterprise GenAI Architecture

Your Data→Delta Lake (Unity Catalog governed)

Training→Mosaic AI Fine-Tuning (Llama 3.1 70B)

Knowledge→Databricks Vector Search (RAG)

Governance→Unity Catalog (row/column security)

Tracking→MLflow (versions, experiments, lineage)

Inference→Enterprise AI Assistant (your model)

What We Build For You

Fine-Tuning on Private Data

We fine-tune Llama 3.1 70B or other open-source models on your proprietary documents, incident history, and domain knowledge — creating a model that truly understands your business.

RAG Pipeline Architecture

Real-time retrieval-augmented generation keeps your model current. Query live ticket states, active incidents, and recent data — all through a governed Databricks vector index.

Unity Catalog Governance

Row-level and column-level security ensures your LLM respects data boundaries. HR data never appears in engineering responses. Full lineage tracking for every inference.

Compound AI Systems

Chain multiple specialized models for complex reasoning. Router models direct queries to the right specialist, orchestrators handle multi-step tasks, evaluators verify output quality.

MLflow Experiment Tracking

Every training run, fine-tune, and evaluation is tracked in MLflow with full reproducibility. Compare model versions, roll back safely, and know exactly which data created which model.

Natural Language Infrastructure

Let executives and non-technical stakeholders query infrastructure in plain English. 'What caused last week's outage?' gets a comprehensive answer from your own model.

Frequently Asked Questions

Why build your own enterprise LLM instead of using ChatGPT?

Commercial LLMs don't know your proprietary data, internal processes, or domain-specific terminology. A custom fine-tuned model answers questions about your specific systems, products, and data — with full privacy and governance. Your data never leaves your infrastructure.

What is Retrieval-Augmented Generation (RAG)?

RAG is a technique that grounds LLM responses in real-time retrieved data. Instead of relying on training data, the model fetches relevant documents from your vector database at inference time — enabling accurate, current answers about live system states, tickets, and incidents.

How does Databricks Unity Catalog protect enterprise data?

Unity Catalog provides centralized governance for all data and AI assets in Databricks. It enforces column-level access controls, row-level security, and full data lineage — ensuring your LLM can only access data the requesting user or agent is authorized to see.

Start With Your Data

We'll assess your data readiness for enterprise LLM fine-tuning — for free.

Request GenAI Architecture Assessment