01 — Detect & research

On-demand company research from any page

Visit any company page — LinkedIn, Greenhouse, Lever, Workday, Ashby, or a generic site — and the side panel detects it automatically. One click triggers research. Nothing fires without a click.

The research pipeline runs Serper for parallel web searches across firmographics, leadership, reviews, job listings, and product overview, then Claude Haiku synthesizes everything into structured JSON. Cached indefinitely — re-researching is on-demand via the Research button so you don't burn API calls.

Detectors: LinkedIn profiles, job postings, company pages — plus Greenhouse, Lever, Workday, Ashby, and a domain-name fallback for anything else.

Coop · side panel
Databricks
Employees7,100
FundingSeries I
IndustryData / AI
HQSan Francisco
Research complete
8
Strong fitPLG motion matches your lane
02 — Save & score

Companies and opportunities are one record. Scored against your real preferences.

Save any company or job posting and it becomes a single record — no duplicate sync between a "company" store and a "job" store. Attach a job description and Coop scores it 1–10 against your background, target roles, salary floor, and work arrangement.

The score reflects how you actually feel, not just what the JD says. A rating you set (1–5 stars) post-processes the score — a role you're excited about gets a lift; one you're lukewarm on gets a haircut. The breakdown shows strong fits, red flags, and qualification matches with full reasoning behind each.

Score colors: ≥7.5 green · 6.0–7.4 amber · 4.5–5.9 orange · below 4.5 red.

Databricksdatabricks.com · San Francisco
Interviewing
Firmographics
Employees7,100
FundingSeries I
IndustryData / AI
Founded2013
Leadership
  • Ali GhodsiCEO, co-founder
  • Naveen RaoVP of AI
Job match
8/10
Strong fitPLG motion, platform narrative, staff scope.
Recent emails
  • Priya NatarajanRe: scheduling intro call
  • RecruiterApplication update
Coop
how's this role actually look for me?
Strong read — the lakehouse positioning work maps directly to your PMM background. One flag: heavy enterprise enablement focus in the JD.
03 — Pipeline

A Kanban that doesn't go stale

Every saved company moves through a fully customizable Kanban. Stages are user-defined. Drag-drop reordering. Stage timestamps record themselves in a generic map — so if you rename stages or add new ones, history doesn't break.

Each card tracks whose court the ball is in (my court / their court), surfaces last activity from emails and meetings, and shows the current score at a glance. Gmail and Granola attach automatically — you don't manually log contact.

Kanban and grid views. Filterable by stage, score, and action owner. Stat cards at the top are clickable and drill into the underlying list.

Interested3
Linear
Head of PMM
9.1my court
Vercel
Director, Product Marketing
7.4their court
Notion
VP Marketing
8.2my court
Applied2
Databricks
Staff PMM
8.0their court
Stripe
Sr. PMM, Payments
7.8their court
Intro1
Ramp
PMM Lead
8.6my court
04 — Apply Queue

Swipe through your pipeline like a reading list

Every saved opportunity that hasn't been triaged lands in a personal queue, pre-scored and annotated with the two or three things that actually matter. Pass or apply in one tap.

Open Application auto-binds Coop in the side panel so you always have full context while you're filling out forms. The queue drains as you triage — the rest of the pipeline moves itself.

Scores show strong fits, red flags, and qualification signals. The queue reorders by score so the best opportunities surface first.

Apply Queue · 6 remaining
Notion
VP Marketing
Remote · $230k–$280k
8/10
Strong fitBrand + PLG narrative scope matches.
PLG motionSalary matchRemote
Stripe
Sr. PMM, Payments
San Francisco · Hybrid · $190k–$240k
7/10
Good fitPayments focus is a stretch from your lane.
FintechHybrid
Databricks
Staff PMM
San Francisco · Hybrid · $210k–$260k
8/10
Strong fitPLG motion, platform story, staff scope.
PLGSalary matchEnterprise flag
05 — Coop, with full context

An advisor that already has the full picture before you ask

Coop auto-binds to whatever entry you're viewing. When you open the chat, he already has your professional profile, the company research, the job description, fit scoring, every email thread with people at that company (via Gmail OAuth), every calendar event, and every Granola meeting transcript.

You stop explaining and start asking. "Given what we talked about with Karri last week, what should I lead with in my application?" And Coop actually knows — not from a prompt you pasted, but from data the extension assembled in the background. You can manually pin Coop to a specific entry with a paperclip button when you want focus across surfaces.

Model fallback chain: GPT-4.1 mini → Claude Haiku → Sonnet → GPT-4.1. UI always surfaces which model actually answered. BYO API keys.

CoopDatabricks · Staff PMM
GPT-4.1 mini
operating principles job posting 2 transcripts
Given my call with Priya last week, what should I lead with in my application?
Lead with the lakehouse positioning work — Priya flagged that the team is rebuilding the enterprise narrative from scratch and explicitly asked if you'd done that kind of foundational positioning work before. Your Notion case study answers that directly.
8/10
Strong fitScope, PLG motion, salary above floor.
Skip the PLG playbook framing for this one — she's focused on enterprise, and leading there risks sounding like a misread of what they need right now.
06 — Operating principles

Code carries mechanics. Settings carry opinions.

A single user-editable textarea in settings carries every interpretation Coop applies to your data. Salary floors, dealbreakers, draft-vs-evaluate behavior, how aggressive you want scoring to be — all of it lives in plain text you can rewrite.

The code emits neutral facts. The textarea tells Coop how to read them. If you want him sharper, rewrite the textarea. If you want him to never refuse a draft request, write that. No code change, no deploy.

This design comes directly from a painful lesson: hardcoding opinions into prompts means the tool argues with the person using it. Ripping that out and handing interpretation to a user-editable config is one of the best decisions in the whole build.

Operating Principles
How Coop interprets your data — rewrite anytime
07 — Local-first architecture

No backend. No accounts. All data stays in your browser.

Coop is a Chrome extension with no server-side component. All pipeline data lives in chrome.storage.local. API keys are user-supplied via an Integrations page — never hardcoded, never leaving the machine.

Gmail access uses Chrome's built-in OAuth (chrome.identity). Granola connects via REST API key. No data passes through a Coop.ai server because there is no Coop.ai server.

Manifest V3 service worker handles all side effects. Every UI surface talks to it through chrome.runtime.sendMessage. Research cached 24h. No API call ever fires without an explicit user action.

Architecture

  • Storagechrome.storage.local — all data on-device
  • Auth (Gmail)chrome.identity OAuth — no tokens on server
  • BackgroundManifest V3 service worker
  • Research cache24h TTL, keyed by company name
  • API keysUser-supplied via Integrations page
  • BackendNone
Chat fallback chain:
GPT-4.1 miniClaude HaikuSonnetGPT-4.1
Skips providers with no key configured. UI shows which model answered.

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