What the Client Was Facing
A private bank's legal team needed to analyse hundreds of contracts daily but were not permitted to use cloud LLM services due to data confidentiality requirements. They were spending 20 hours per week on tasks that AI could handle — but had no safe way to deploy AI.
What ZippyOPS Was Engaged To Do
ZippyOPS was brought in to design and implement a solution addressing the root causes of the client's challenges — delivering measurable outcomes within a fixed engagement timeline. Our team worked embedded with the client's engineers throughout the entire project.
How We Solved It
ZippyOPS deployed LLaMA 3 70B on the bank's private GPU infrastructure using vLLM for high-throughput inference. A RAG pipeline was built connecting the model to the document management system using LangChain and Qdrant as the vector database. RBAC ensured lawyers only accessed their own client documents.
Technologies Used
Measurable Outcomes Delivered
Legal team document analysis time reduced 75% — 20 hours/week saved
Zero data leaves the bank's infrastructure — full regulatory compliance maintained
Model answers cited with source document references for auditability
3 additional departments onboarded to the internal AI platform within 4 weeks
Want Similar Results for Your Team?
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