finance_ops · services · workflow
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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Client submits business query
Clients use an intuitive, language-based interface to make insightful, data-driven decisions about their businesses and industries.
Tools used
DatabricksVector SearchMixtral 8x7BRAGUnity CatalogDatabricks Foundation Model APIsDatabricks Model ServingLlama 2 13BDatabricks GenAI Advisory Program
Outcome
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.
What failed first
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.
Results
Time savedless than six weeks
Grounding & classification
Source type: vendor customer story
27 fields verified against source quotes.
knowledge searchragsummarizationknowledge basefailure mode describedmetric backednamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedprofessional servicescycle time reductionvendor customer storyfinance opsdata sync enrichmentrag answering