The case for Thesis
General-purpose AI can read a document. It can't remember your firm's last 200 deals, score against your specific thesis, or hand context to the next analyst who picks up a deal. Thesis is built around how venture investing actually works, deal after deal, year after year.
Analysts leave. Notes get lost in old email threads. Thesis keeps every memo, score, and decision in one place, so the firm's judgment compounds instead of resetting every time someone moves on.
A general-purpose model can summarize a pitch deck. It has no idea what your fund actually invests in, what stage you focus on, or what's killed deals for you before. Thesis fit scoring is calibrated to your firm specifically.
Saving 40 minutes on one deck is nice. Saving 40 minutes on 50 decks a week is the difference between triaging your pipeline properly and falling behind on it.
Six months after passing on a deal, can your team explain why? Thesis keeps the reasoning attached to the deal, so the firm can actually learn from outcomes instead of just moving on to the next one.
An analyst reviewing 20 decks a week at 45 minutes each spends about 15 hours a week on extraction and first-pass write-ups alone, before any actual judgment happens. At even a junior analyst's loaded cost, that is a meaningful chunk of a work week spent on work that does not require a human to do well.
Thesis brings that 45 minutes down to under 30 seconds of processing time, plus a few minutes for the analyst to read the output and form their own opinion. Across 20 decks a week, that is the difference between spending 15 hours a week on triage and spending under 2.
For a firm with 10 analysts on a shared workspace, that time difference compounds. The Firm plan is flat at $99 a month for the whole team, unlimited decks, which generally costs less than a single hour of one analyst's time.