Engineering

Meet Krew, a platform where Docusign engineers can build an AI teammate wired into the company’s own tools, able to do whatever repetitive task engineers throw at it. One watches for a flagged PR, another chases down an overnight bug, another checks a feature flag rollout, and each hands off to a person before anything ships. We've built more than 220 Krewmates and they're already saving thousands of engineering hours.

Shreeya Dhakal spends her days evaluating Docusign's AI agents, and her nights building NLP tools for Nepali, her native language and a low-resource one she's worked to bring into the computational world since college. In this installment of Meet the Engineer, the Applied Scientist talks about debugging an AI agent that gave different answers to the same question, and what "good enough to ship" actually means in a multi-tool agentic system. She also explains how building tools for her own language taught her to notice when a system is quietly failing, long before the aggregate metrics would show it.

Shankar Gopalakrishnan has been building storage platforms longer than Amazon S3 has existed. In this first installment of Meet the Engineer, the senior director of engineering behind Docusign's multi-cloud storage platform talks about sequencing a migration without destabilizing live services, the hardest problem customers will never see, and why long-distance cycling is a perfect model for how complex systems behave under pressure.

Running a frontier model on every document doesn't scale at 1M+ documents a day. In part two of our series on how Docusign processes agreements at scale, we cover how we trained a smaller, faster model to match — and in production, beat — the accuracy of the larger model it replaced, at a fraction of the cost.

How did Docusign take AI coding adoption from single digits to near-universal in about a year? Not with a top-down mandate, but with a peer-led program designed to establish guardrails and embed AI coding into individual development teams. We detail our journey, from an early pilot to a grassroots multiplier network to a suite of internal AI agents now automating code review and repetitive engineering work.

Docusign's AI engine processes over a million agreements a day, extracting more than 50 data points per document at sub-cent inference cost. This is the first post in a four-part series on how we built that pipeline, starting with the orchestration architecture that routes every document through pre-processing, model inference, and validation at enterprise scale.