MNP builds a RAG-based GenAI client insights platform on Databricks in under six weeks
MNP's existing systems could not support the processing required for advanced ML applications, and their initial GenAI deployment faced integration constraints, response latency, and prohibitive GPU costs that made scaling a client insights solution impossible.
MNP's initial GenAI deployment was blocked by its foundation model's tight coupling to the existing data warehouse, time-to-first-token latency issues, and GPU costs that became prohibitive at the scale needed for a reliable corpus.
MNP developed a GenAI solution delivering accurate responses in less than six weeks, with the data team building a model from start to quality assurance testing within four weeks, while maintaining data isolation via Private AI standards.
Frequently asked questions
What did this team achieve with this AI workflow?
MNP developed a GenAI solution delivering accurate responses in less than six weeks, with the data team building a model from start to quality assurance testing within four weeks, while maintaining data isolation via…
What tools did this team use?
Databricks, Vector Search, Mixtral 8x7B, RAG, Unity Catalog, Databricks Foundation Model APIs, Databricks Model Serving, Llama 2 13B, Databricks GenAI Advisory Program.
What results were reported?
GenAI solution development time: less than six weeks; model build to QA testing time: within four weeks (source-reported, not independently verified).
What failed first in this deployment?
MNP's initial GenAI deployment was blocked by its foundation model's tight coupling to the existing data warehouse, time-to-first-token latency issues, and GPU costs that became prohibitive at the scale needed for a r…
How is this finance ops AI workflow structured?
Client submits business query → Data consolidated into lakehouse → Vector Search retrieves embeddings → RAG integrates retrieved context → LLM generates summarized insights → Insights delivered to client.