An agent pipeline that reads the codebase, explores the live app read-only, records evidence for everything it sees and checks each claim against both. Only then does it write the manual, every statement linked to its evidence.
[ fig. 1 ]A walkthrough of Docsy: it reads a codebase, explores the live application, records evidence, checks each claim against code and runtime, flags where they disagree, and writes a manual whose statements link to their evidence. Recording, 0:04–0:19.
01The problem
Client manuals were written from memory — days of clicking through the product and taking screenshots — and went out of date with the next release. AI writers only guessed faster: fields that don't exist, timings nobody measured.
Before
I think this is how it works
After
I saw this
02How it’s built
Every claim checked twiceHarbour Wellness demo · fictional
Codecode:route-definedGET /admin/login in src/app.js:79
Claimcl-0006The “Harbour CMS” screen is available at /admin/login.Verified
Live appruntime:route-loadedLoaded /admin/login
Manual1. Open the sign-in page at /admin/login. Verified · cl-0006Only verified claims are written in
DiscrepanciesD-01: Therapy “Summary” is required in code but optional in the CMS
[ fig. 2 ]A drawing of the system, not a screen. Swipe sideways to see all of it.
03In numbers
136
claims checked
0
claims without evidence
4/4
planted discrepancies found
On Docsy's demo app: 136 claims in the ledger, 124 of them verified in both the code and the live app, and all 4 planted discrepancies found.