layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Learning & Knowledge Systems Lead at Harper | LAYIQ

Two tracks, running in parallel, at the intersection of Operations, Engineering, and RevOps.
Build the operating memory.
Embed with sales, intake, service, placements, and renewals. Sit with operators, listen to calls, shadow workflows, and document what people "just know."
Turn transcripts, Slack threads, Looms, and one-off explanations into source-of-truth docs, decision logs, playbooks, process maps, glossaries, and system-boundary docs (what each internal system does, where one stops and the next begins, and what gets misread).
Graduate stabilized rules into skills the agents can call
, and partner with engineering on refresh automations so docs stay alive instead of going stale.
Hunt the edge cases — reworks, escalations, stale quotes, market follow-ups, binder/payment gaps, customer confusion — and write them down before they bite again.
Get the company to run against it.
When a rich AI-generated playbook lands, distill it into an executable plan: named owners, the first three moves, a rollout cadence and date. The plan doesn't run itself; you make it run.
Build the onboarding paths and setup scripts that get a new hire into Cursor, Claude Code, and the harness within a week.
Run cohort rollouts, drive adoption, and make activity visible — we should know who's actually in the harness.
Shape meetings in real time so they produce useful artifacts: decisions, owners, definitions, edge cases, open questions, next steps.
When the same problem shows up three times, turn it into a playbook, a QA check, a skill, or a product requirement. You'll work directly with the CEO when extraction calls for it.
Who you are
An exceptional writer and synthesizer. You can take a messy transcript to a clear operating doc, and a clear doc to something a team actually executes against.
AI-native in practice — this is the bar.
Not "I use ChatGPT." You have taste for when an output is structurally wrong, not just stylistically off. You prompt for extraction (decisions, contradictions, owners, edge cases), not just summarization. You know good output depends on good context, and you engineer that context upstream. Cursor, Claude Code, MCP servers, agent memory files, Granola, structured prompting are tools you reach for without thinking.
Genuinely curious about how organizations work, and happy to sit with operators to find out.
Structured but not bureaucratic. You care whether documentation changes behavior, not whether it looks polished.
Able to operate in chaos without becoming chaotic. Low-ego, persistent, and allergic to "someone should probably document that."
Backgrounds that can work
(the skill matters more than the title): modern enablement at an AI-native company, qualitative/academic research, ethnography, instructional or curriculum design, knowledge management, product ops, technical writing, research ops, implementation, chief of staff roles, library and information science, AI ops / human-in-the-loop work, or startup operations where the systems were messy and someone had to make them legible.

Learning & Knowledge Systems Lead

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