Applied LLM on Databricks

A governed RAG solution on Databricks, built on your Delta Tables and deployed in fixed scope.

Performance has degraded. Costs are climbing. Jobs are failing in ways that aren't obvious. We diagnose and resolve specific Databricks problems, from root cause to fix, with documentation your team can operate from after.
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Key Benefits

What the solution delivers
for your organisation

Increase developer productivity

Developers query the LLM directly rather than building bespoke retrieval logic, the pipeline handles ingestion, embedding, and serving.

Built on top of your own Delta Tables

No separate data store. The LLM retrieves from your existing Delta Tables, governed by the same Unity Catalog policies as the rest of your Lakehouse.

Reduce time to market

A fixed-scope engagement with defined phases and deliverables gets the application into production without open-ended discovery.

Improve security by keeping your data and model in-house

The entire stack runs inside your Databricks managed environment. No proprietary data passes through an external API.

Easily monitor the performance of your LLM solution

Automated monitoring is part of the delivery, not a later addition. Response quality and usage patterns are tracked from day one.

Reduce total cost of ownership (TCO)

Running the solution on your existing Databricks infrastructure avoids the cost of standing up and maintaining a separate AI stack.

The Architecture

High-level RAG LLM architecture on Databricks

The pipeline runs end-to-end inside the Databricks managed environment from raw data ingestion through to model serving and user queries. Open-weight models, Databricks hosted foundation LLMs, and third-party API models are all supported. The architecture adapts to your data type, volume, and governance requirements.
Scope of Work & Deliverables

From strategy to a
monitored, deployed LLM.

Every Applied LLM engagement runs through the same four phases. Scope is fixed at the start. Each phase has a defined output. The engagement ends with a production LLM application and a monitoring setup your team can operate independently.
01
Advisory

We assess your knowledge base, define the use case, select the model approach (RAG, fine-tuning, or hybrid), and confirm the architecture. Model selection - Databricks-native, Hugging Face, or third-party API - is locked here based on your data type, volume, and governance requirements.

LLM strategy document
Target architecture brief
02
Solution Building

End-to-end build of the LLM application: data preparation, embedding pipeline, vector search setup, RAG architecture or fine-tuning, and Q&A layer integration. Tested against your data and use case before moving to serving.

Validated, deployable LLM application
03
Model Serving

Deployment into your Databricks workspace via Model Serving - a managed, scalable endpoint accessible to your applications and users. Access-controlled through Unity Catalog. Internal teams are involved throughout.

Live LLM endpoint in production
04
Monitoring

Monitoring dashboard and automated alerting configured to track query performance, response quality, and usage volume. Baseline metrics set. Refresh and retraining triggers defined.

Operational monitoring setup your team can act on independently
Packages

Three tiers. Fixed scope.
Choose where to start.

All three packages follow the same four phases. The tier determines depth of model customisation, monitoring maturity, and flexibility for your team to extend the solution post-delivery.
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Fundamental
TRANSITIONAL
PERSONALIZED
Define an LLM strategy based on your requirements
Setting up Databricks Vector Database using your own data
Setting up a pre-trained generative language model (either from Databricks Marketplace or a third-party provider using an API)
Building an ML pipeline for the Q&A chatbot
Select Vector Database to store your vectorized data
Use custom models from Huggingface repository
Setting up monitoring to easily track how your model performs
Model fine-tuning and providing you the ability to further fine-tune it on your own
Model comparison between multiple generative models

Ready to put your knowledge
base to work?

Tell us your use case, your current Databricks setup, and your governance requirements. We'll confirm the right package and walk you through what the architecture looks like for your environment.
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