Project Active

AccountStud

An AI account-intelligence product for enterprise AEs that turns a book of accounts into a daily plan.

Type
Project
Status
Active
Started
2026-05-02
Themes
sales-technology, product-design, ai

Why it exists

As an enterprise account executive, I never lacked account data. What I lacked was a clear answer to "what should I do at 9am?" AccountStud is the product I'm building to answer that question. I'm a co-founder and lead product and design; my technical co-founder leads engineering.

Approach

A command center, not a table. The home screen replaces the raw account-book view with an action-oriented daily surface: a tiered account watchlist (Uranium down through Bronze), a quota attainment bar, an action queue, a signal feed, and a calendar strip. I borrowed mechanics from Robinhood's watchlist, Superhuman's triage, and Duolingo's "continue where you left off."

Make Your Number. A quota-gap calculator that works backward from the target: deals needed, then meetings needed, then accounts to work. It sorts the result into three buckets: untouched priority accounts, stale accounts with fresh signals, and thin-coverage accounts. A one-page plan export for manager 1:1s is designed for a later version.

Campaign workflow. A five-step flow: Account Intelligence, Prospect Upload, Org Chart Builder, Campaign Build, Outreach. It always resumes at the exact step you left.

A spec system for two founders. I set up a Notion, Linear and Slack workflow that moves an idea through capture, spec, review and ticket. It includes a Spec Hub database, a product map organized by surface, a decision log and a founder-sync template.

Feasibility work. I researched bring-your-own-key architecture and enrichment-provider integration before committing to either.

What I learned

Good ideas that live in one person's head don't survive contact with a two-person team. The most valuable early work wasn't a feature, it was a shared product language and a spec process the engineer could trust. I also learned to design from the user's question rather than the data model: the useful home screen answers what to do next, not what exists.