Legal Firm Automates Document Discovery with Production-Grade LLMOps

The Challenge

A top-tier law firm was spending thousands of associate hours on manual document discovery. They had experimented with basic AI tools, but they were slow, inaccurate, and lacked the security needed for sensitive client data. They needed a production-ready AI pipeline that could handle millions of documents with precision.

What We Didn't Do

We didn't suggest a 'one-size-fits-all' legal AI SaaS that would store their data in a proprietary silo. We didn't build a brittle, one-off script that would break with the next model update.

The SenseSys Approach

We built a custom, enterprise-grade RAG (Retrieval-Augmented Generation) pipeline hosted on their private cloud. We implemented strict LLMOps for model versioning, testing, and monitoring. Our solution used a combination of vendor-neutral open-source models to ensure they weren't locked into one provider. We prioritized data privacy, ensuring all embeddings and retrievals stayed within their encrypted perimeter.

The Results

• Reduced document review time by 75% for initial discovery
• Achieved 94% accuracy on entity extraction and summarization
• Maintained 100% data residency within firm-controlled infrastructure
• Deployed to production in 12 weeks with full internal training

AI in legal isn't about the model; it's about the pipeline. LLMOps ensures your AI is reliable, secure, and accurate.