Your Agreements Are a Database You Can't Query. We're Fixing That

Official Schedule Context

Agreements power every enterprise business, but the most critical data — pricing schedules, SLA

obligations, rate cards — is often trapped in tables that traditional extraction tools destroy.

This session shows what changes when you can actually extract that data accurately at scale and make

it searchable. We'll walk through the before and after: Before: Contract tables require manual

review. Rate cards are buried. SLA terms are scattered across exhibits. Procurement teams spend

hours piecing together pricing structures — and searching for specific terms means opening every

document. After: Tables are automatically extracted, structured, and queryable. Operations teams can

surface SLA notification requirements on demand. Legal can answer "what hourly rate did we agree

to?" in seconds. Docusign will share what we've achieved evaluating NVIDIA Nemotron Parse for our

document processing pipeline, including how we tested against real enterprise contracts (not

synthetic benchmarks), why we're serving the model via vLLM, and what it takes to turn extracted

table data into searchable, retrievable agreement intelligence. NVIDIA will cover the architecture

behind Nemotron Parse and where the model is heading — including how NeMo Retriever's embedding and

reranking models connect extracted data to search and RAG-based applications. Attendees will leave

with a realistic view of where vision-language models excel at document understanding, where the

gaps remain, and how to think about building searchable contract intelligence into their own

systems.

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