You turn the judgment locked inside Harper’s best operators into AI-legible knowledge — living docs, decision logs, and retrievable skills the agents can actually call — and you get the rest of the company running against it.
AI doesn’t understand a company by default. It works only when the business is documented clearly enough for a system to retrieve the right context, recognize the workflow, handle the edge case, and escalate when human judgment is required.
Right now most of how Harper operates lives in people’s heads: how a top rep sequences quotes, how service handles the weird bind, how market routing actually works, what a customer means when they push back. That holds at small scale. It breaks at ~1,000 new customers a month. Every undocumented process is a future failure mode; every AI-generated playbook that dies in a chat thread is throughput left on the floor.
The next bottleneck here isn’t engineering. It’s knowledge — and how fast people can absorb it. This role removes that bottleneck.
Be clear about what this is not. This is not corporate L&D. No LMS, no slide decks, no e-learning project, no making-the-Notion-pretty. This is knowledge engineering: sit with operators, extract how they actually think, and turn it into structured knowledge a human and a model can use.