Good product thinking
Careful thinking shouldn't cost you a sprint. Thesis does the slow parts: AI drafts the tests, they all run at once, evidence arrives from your tools, and your own agents keep it up to date. Every underlined sentence is a bet. Tap one.
A test plan in seconds, not a sprint
Write the claim. Jev works out whether it's a desirability, usability, viability, or feasibility bet, then drafts the smallest experiment that could prove it wrong. You decide what runs.
Test every bet at once
Stop arguing about which assumption goes first. Cheap tests run side by side, so the whole thesis gets checked in days instead of a quarter, and results land as they arrive.
Evidence that shows up by itself
Connect Amplitude, Dovetail, PostHog, Linear, and Slack. Funnels and interview quotes land on the assumption they back, or break, with no copying and pasting. A closed ticket never marks a bet as proven.
Know what a bet is worth before you build it
Size an opportunity like a hand of cards: who it reaches, times what each one is worth, cut by what reality takes. The range is as wide as your evidence, and the card that moves it most is the one to test first.
One thesis for the whole team
Product, design, data, and engineering see the same bets and the same evidence. Decisions take a comment, not a meeting, and nobody re-argues the plan in Slack.
Your agents work in Thesis too
Connect Claude Code, Cursor, Codex, or any agent over MCP or the API. They add assumptions, log evidence, and comment under their own name, so the thesis keeps up without anyone opening it.
Engineering builds what you're betting on
Hand the thesis to your developers and their coding agents as a skill or a repo file. Thesis checks each pull request against your bets, so the work and the plan stay aligned.
Questions
Start with one bet.
We're running pilots with product teams of 20 to 80 people who want to move fast without guessing.