--- schema: 1 type: video title: >- Your company brain will leak secrets: how we stopped it for big banks — Tanmai Gopal, PromptQL description: >- Tanmai Gopal plotted the daily edits to his own company brain expecting the usual shape, a burst of enthusiasm followed by neglect. The line kept climbing instead, and it surprised him. His reading is that a system people trust gets taught more, not less: teach it to query the data, then to interpret the result, then to act on it, and each skill adds its own steady rate of correction on top. A rising edit count is what health looks like. Gopal cofounded PromptQL and before that built the Hasura GraphQL engine, and his team spent a year deploying an early company brain across 15 to 20 organizations, from AI native startups to Fortune 100 banks. Their own brain runs to about 5,000 interconnected pages. The obstacle is that a shared brain leaks. He dismisses two common answers before offering his. Nobody writes shared skills in GitHub for a colleague they have never met, and a team brain that saves its own memory is just a fresh silo, the same trap as per channel memory. His third option puts everything in one companywide wiki of linked markdown files, scopes read and write access per file, and refuses to let the agent write on its own. The agent proposes the facts and the scopes; a person accepts, and their name goes on the change so a leak has an owner. For the multiplayer case, where several people debug an incident together and the argument itself produces the best knowledge, credentials never sit in the sandbox. They are injected per user at the HTTP and SQL layers. Timestamps: 0:00 - Why company brains leak, and what holds deployment back 1:06 - From the Hasura GraphQL engine to PromptQL 2:03 - Three kinds of customer, from startups to banks 3:14 - Modeling the brain, and 5,000 linked pages 3:41 - A poll: what does a healthy edit curve look like 5:16 - The line that kept climbing 6:37 - Two use cases, personal recall and shared work 9:50 - Grow one, do not build one 11:09 - A security questionnaire answered end to end 12:33 - Why nobody writes shared skills in GitHub 13:52 - Why a team brain is just another silo 14:59 - One wiki, scoped files, and no silent writes 18:45 - Every change carries a human name 20:06 - The multiplayer case, where the argument is the knowledge 23:17 - Privilege escalation, and credentials outside the sandbox --- Watch along with the corrected transcript, summary, timestamps and resources on this talk's official AIE page: https://ai.engineer/talks/0uC6u0lJJl4-youtube-04c70e929b8db2fa2d6c Join our upcoming AI Engineer conferences (see https://ai.engineer landing page): - AIEi Shanghai, November 5–6 — the first for meeting top Chinese labs and engineers - AIE CODE in SF, November 10–12 — the top AI Coding conference returns - AIEi Sydney, December 7–8 — the first AIEi run alongside NeurIPS 2026 in Sydney --- canonical: 'https://liquidgold.cc/watch/0uC6u0lJJl4' markdown: 'https://liquidgold.cc/watch/0uC6u0lJJl4.md' llms: 'https://liquidgold.cc/watch/0uC6u0lJJl4/llms.txt' parent: 'https://liquidgold.cc/channel/aidotengineer.md' channel: aidotengineer updated: '2026-10-24T04:00:08.656Z' language: null children: [] videoId: 0uC6u0lJJl4 durationSeconds: 1585 publishedAt: '2026-09-03T15:30:26.000Z' searchChunks: - ordinal: 0 text: null startSeconds: 23 endSeconds: 67 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=23' heldReason: rights - ordinal: 1 text: null startSeconds: 23 endSeconds: 67 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=23' heldReason: rights - ordinal: 2 text: null startSeconds: 144 endSeconds: 202 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=144' heldReason: rights - ordinal: 3 text: null startSeconds: 144 endSeconds: 202 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=144' heldReason: rights - ordinal: 4 text: null startSeconds: 326 endSeconds: 404 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=326' heldReason: rights - ordinal: 5 text: null startSeconds: 415 endSeconds: 592 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=415' heldReason: rights - ordinal: 6 text: null startSeconds: 415 endSeconds: 592 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=415' heldReason: rights - ordinal: 7 text: null startSeconds: 602 endSeconds: 677 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=602' heldReason: rights - ordinal: 8 text: null startSeconds: 766 endSeconds: 840 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=766' heldReason: rights - ordinal: 9 text: null startSeconds: 851 endSeconds: 907 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=851' heldReason: rights - ordinal: 10 text: null startSeconds: 919 endSeconds: 1133 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=919' heldReason: rights - ordinal: 11 text: null startSeconds: 919 endSeconds: 1133 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=919' heldReason: rights - ordinal: 12 text: null startSeconds: 1133 endSeconds: 1144 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=1133' heldReason: rights - ordinal: 13 text: null startSeconds: 1144 endSeconds: 1212 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=1144' heldReason: rights - ordinal: 14 text: null startSeconds: 1226 endSeconds: 1398 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=1226' heldReason: rights - ordinal: 15 text: null startSeconds: 1398 endSeconds: 1409 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=1398' heldReason: rights - ordinal: 16 text: null startSeconds: 1409 endSeconds: 1585 watch: 'https://liquidgold.cc/watch/0uC6u0lJJl4?t=1409' heldReason: rights --- # Your company brain will leak secrets: how we stopped it for big banks — Tanmai Gopal, PromptQL Tanmai Gopal plotted the daily edits to his own company brain expecting the usual shape, a burst of enthusiasm followed by neglect. The line kept climbing instead, and it surprised him. His reading is that a system people trust gets taught more, not less: teach it to query the data, then to interpret the result, then to act on it, and each skill adds its own steady rate of correction on top. A rising edit count is what health looks like. Gopal cofounded PromptQL and before that built the Hasura GraphQL engine, and his team spent a year deploying an early company brain across 15 to 20 organizations, from AI native startups to Fortune 100 banks. Their own brain runs to about 5,000 interconnected pages. The obstacle is that a shared brain leaks. He dismisses two common answers before offering his. Nobody writes shared skills in GitHub for a colleague they have never met, and a team brain that saves its own memory is just a fresh silo, the same trap as per channel memory. His third option puts everything in one companywide wiki of linked markdown files, scopes read and write access per file, and refuses to let the agent write on its own. The agent proposes the facts and the scopes; a person accepts, and their name goes on the change so a leak has an owner. For the multiplayer case, where several people debug an incident together and the argument itself produces the best knowledge, credentials never sit in the sandbox. They are injected per user at the HTTP and SQL layers. Timestamps: 0:00 - Why company brains leak, and what holds deployment back 1:06 - From the Hasura GraphQL engine to PromptQL 2:03 - Three kinds of customer, from startups to banks 3:14 - Modeling the brain, and 5,000 linked pages 3:41 - A poll: what does a healthy edit curve look like 5:16 - The line that kept climbing 6:37 - Two use cases, personal recall and shared work 9:50 - Grow one, do not build one 11:09 - A security questionnaire answered end to end 12:33 - Why nobody writes shared skills in GitHub 13:52 - Why a team brain is just another silo 14:59 - One wiki, scoped files, and no silent writes 18:45 - Every change carries a human name 20:06 - The multiplayer case, where the argument is the knowledge 23:17 - Privilege escalation, and credentials outside the sandbox --- Watch along with the corrected transcript, summary, timestamps and resources on this talk's official AIE page: https://ai.engineer/talks/0uC6u0lJJl4-youtube-04c70e929b8db2fa2d6c Join our upcoming AI Engineer conferences (see https://ai.engineer landing page): - AIEi Shanghai, November 5–6 — the first for meeting top Chinese labs and engineers - AIE CODE in SF, November 10–12 — the top AI Coding conference returns - AIEi Sydney, December 7–8 — the first AIEi run alongside NeurIPS 2026 in Sydney --- Canonical: https://liquidgold.cc/watch/0uC6u0lJJl4 · Channel: [AI Engineer](https://liquidgold.cc/channel/aidotengineer.md) > Search chunk text is held until public-source provenance is verified. ## Document chunks > Missing: no stored document chunks are available.