The Problem

36 million businesses in America need insurance—it's not optional. 77% are underinsured. 40% have no coverage at all. The distribution system failed them: too slow, too opaque, too confusing.

Over 90% of commercial insurance is still human-led. We're building the inverse: 90%+ AI-led, pushing toward the higher 90s. But to do that, everything we do has to become legible.

Today, most of Harper's operating knowledge lives in people's heads—how a top rep prioritizes quotes, how service handles edge cases, which underwriter to chase, what a customer really means when they push back at bind, why a workflow changed yesterday. That works at small scale. It breaks at ~1,000 new customers a month.

We need someone to turn tribal knowledge into operating memory.

The Thesis

AI doesn't magically understand a company. It only works when the business is documented clearly enough for systems to retrieve the right context, recognize the workflow, handle the edge cases, and escalate when human judgment is needed.

The next bottleneck at Harper is not engineering. It's knowledge. Every process that lives only in someone's head is a future failure mode. Every undocumented edge case is another rework. Every meeting decision that disappears is a repeated argument. Every workflow that's not clear enough for a new hire is not clear enough for an AI agent either.

You'll turn messy operating reality into structured, AI-legible knowledge—and make sure Harper's knowledge compounds instead of disappearing.

The Role

You embed with teams across sales, intake, service, placements, and renewals. You sit with operators while they work. You listen to calls, read transcripts, study workflows, and figure out what's actually happening underneath the chaos.

Then you turn that into operating memory: source-of-truth docs, SOPs, playbooks, decision logs, process maps, onboarding paths, glossaries, and AI-readable knowledge bases.

You don't need to be an engineer. You do need to be exceptional at using AI tools—Granola, Claude, ChatGPT, transcript workflows, structured prompting, deep research, AI-assisted synthesis—to turn raw context into reusable work product.

A big part of the job is leaving breadcrumbs for AI. Shaping conversations so transcripts are useful later. Asking the questions in real time that turn a meeting into an artifact: who owns this, what's the exception, what's the source of truth, what would someone misunderstand from the transcript alone, is this a temporary workaround or the new process.

This is not a note-taking role. This is not "make the Notion pretty." This is an operating role for someone who can walk into ambiguity, find the hidden logic, and turn it into systems.

What You'll Do

Capture tribal knowledge — Interview operators, shadow workflows, sit with teams, and document what people "just know"
Build operating memory — Turn transcripts, Slack threads, Looms, and one-off explanations into source-of-truth docs, decision logs, playbooks, and process maps
Use AI as a force multiplier — Build repeatable workflows that turn raw context into decisions, owners, open loops, SOPs, training material, and product requirements
Make meetings AI-legible — Shape conversations in real time so they produce useful artifacts: decisions, owners, definitions, edge cases, unresolved questions, next steps
Find the edge cases — Document where workflows break: reworks, escalations, stale quotes, underwriter follow-ups, payment/binder gaps, COI delays, customer confusion
Translate ops into product — Sit between operators and engineering. Capture what people are doing, where tools fail, what workarounds exist, what needs to be built
Maintain the knowledge base — Keep docs current, assign owners, kill stale guidance, make sure people know where the truth lives
Turn repeated problems into systems — If the same issue happens three times, it becomes a playbook, a QA check, a training artifact, or a product requirement

You Might Be a Fit If…

You're an exceptional writer and synthesizer
You're curious about how organizations actually work, and you like sitting with operators
You can turn a messy transcript into a clear operating doc the same day
You're AI-native in practice—not just "I use ChatGPT," but you have taste for when an output is structurally wrong, not just stylistically off
You know how to prompt for extraction (decisions, contradictions, owners, edge cases) not just summarization
You understand that good AI output depends on good context, and you know how to engineer context upstream
You notice hidden assumptions, missing ownership, and contradictions other people walk past
You're structured but not bureaucratic
You care whether documentation changes behavior, not whether it looks polished
You can operate in chaos without becoming chaotic
You're low-ego, persistent, and allergic to "someone should probably document that"

Backgrounds That Could Work

We're open-minded. Strong candidates might come from qualitative or 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 roles where the systems were messy and someone had to make them legible.

The exact background matters less than the ability to extract knowledge, impose useful structure, use AI tools well, and create artifacts that change how people work.

Requirements

2–8 years in research, product ops, knowledge management, technical writing, implementation, chief of staff work, qualitative research, instructional design, or startup operations
Exceptional written communication
Strong AI-tool fluency: Claude, ChatGPT, Granola, transcript workflows, structured prompting, AI-assisted synthesis
Demonstrated ability to interview stakeholders and extract operational detail
Ability to turn messy conversations into clear decisions, workflows, and source-of-truth documentation
Strong information-architecture instincts
Comfort in a fast-moving, ambiguous startup
Based in San Francisco or willing to relocate

Nice to Have

Experience at a high-growth startup
Background in qualitative research, ethnography, curriculum design, or library/information science
Experience working with sales, service, customer success, or operations teams
Experience with RAG/search systems, data labeling, human-in-the-loop QA, or internal automation
Experience translating operator feedback into product requirements
Experience building internal knowledge bases in Notion, Confluence, Guru, Coda, or similar
Experience with taxonomy, metadata, tagging, or content governance
Experience in insurance, fintech, B2B services, or another high-volume operational environment

Compensation

Salary: $90,000–$150,000 + performance bonuses & equity
Location: San Francisco, in-office
Schedule: Monday–Friday, in-office hours that match the rest of the company. The hours are long. The people who thrive here wouldn't have it any other way.

Benefits

Health, dental, and vision insurance
Commuter benefits
Team meals and snacks
High ownership, high visibility, and direct exposure to how an AI-native company is built

The Process

15-minute founder call — Alignment on mission, pace, and role fit
Work sample — Turn messy source material into structured operating memory using AI tools

Operating Memory Lead