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One Brain to Query: Wiring a 60-Person Company into a Single Slack Bot
- meryll_dindin 6mo agoWe're a 30-person ed-tech company. I built a Slack bot that connects our data warehouse, 250k Google Drive files, support tickets, and codebase so anyone on the team can ask it a question and get a sourced answer back. The bot took two and a half weeks to build; the data infrastructure under it took two years. Wrote up the architecture, where trust breaks down, and what I'd build first if starting over.
- croemer 6mo agoIs the result good? Is it useful? And why does your comment say you're a 30-person company but the title says 60?
- jedberg 6mo ago> And why does your comment say you're a 30-person company but the title says 60? AI hallucination? :)
- meryll_dindin 6mo agoBecause it depends on how you look at it, given we're effectively a one-sided marketplace too - arguably, we are also 350+ people ;)
- r1290 6mo agoI just did the exact same thing for my company. I didn’t do the sql lite approach for gdrive though just a direct search. The one part that is still difficult is the data modeling and table level descriptions etc. Maybe you make an update to a table - remove a column, etc. The 3rd party systems all have their schemas defined but the data warehouse is a bit more loose. So solving that really helps. Did you just use dbt schema to describe tables and columns then sync that to your bot? How did you keep it updated? And end of the day - worth building or buying? Also how did you track costs? I let users choose their model - but have learned it can get expensive fast. As I can see there are a lot of providers trying to solve this one thing. That said the data warehouse aspect is the loosely defined area and I can see dbt or one of those players try to build something.
- mannanj 6mo agoHi. thanks for sharing. One thing I'd like to know is how often do you validate the answers? If a human gives an answer like the one the AI is giving for example, you'd probably expect a margin of error of like 1% of making a mistake. The AI though, is it 1% or less - and who's validating it? Are you trusting it more or less than a human?
- bob1029 6mo ago> The bot took two and a half weeks to build; the data infrastructure under it took two years. This is the key lesson that everyone needs to step back and pay attention to here. The data is still king. If you have a clean relational database that contains all of your enterprise's information, pointing a modern LLM (i.e., late 2025+) at it without any further guidance often yields very good outcomes. Outcomes that genuinely shocked me no fewer than 6 months ago. I am finding that 100 tables exposed as 1 tool performs significantly better than 100 tables exposed as 10~100 tools. Any time you find yourself tempted to patch things with more system prompt tokens or additional tools, you should push yourself to solve things in the other ways. More targeted & detailed error feedback from existing tools often goes a lot further than additional lines of aggressively worded prose. I think one big fat SQL database is probably getting close to the best possible way to organize everything for an agent to consume. I am not going to die on any specific vendor's hill, but SQL in general is such a competent solution to the problem of incrementally revealing the domain knowledge to the agent. You can even incrementalize the schema description process itself by way of the system tables. Intentionally not providing a schema description tool/document/prompt seems to perform better with the latest models than the other way around.
- operatingthetan 6mo agoI think context windows are too small for an agent to actually do this properly yet. I have much smaller databases and with 1b context frontier models they still need reminders or nudging or come up with completely wrong stuff in response to basic queries. Having the c-levels relying on this for off-the-cuff info seems ... dangerous?
- rick1290 6mo agoAgreed. When I watch the llm start to explore the db - it really does impress me. Can you expand on this: You can even incrementalize the schema description process itself by way of the system tables. Intentionally not providing a schema description tool/document/prompt seems to perform better with the latest models than the other way around.
- bob1029 6mo ago
- truelson 6mo agoJust going to say it... no mention of handling the security aspects of this. Scary. This is cool, I should say, but I would be really worried about the security aspects. Prompt injection here could be really painful.
- georgeburdell 6mo agoThe article mentions that there’s an identification process and that at least some data has access control. What were you expecting?
- truelson 6mo agoYou're wiring up a number of critical systems... and prompt injection here could be really bad. I worry about such systems with a single point of contact
- jedberg 6mo agoReading through it, I didn't see any mention of write access. It looks like the agent is strictly read-only with access controls.
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- mritchie712 6mo ago> The data infrastructure underneath it took two years. yep, that's what Definite is for: https://www.definite.app/ https://www.definite.app/ All the data infra (datalake + ELT/ETL + dashboards) you need in 5 minutes.
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- RobRivera 6mo agoIf I order now, do I get a second set for free?
- deleted 6mo ago[deleted]
- maxothex 6mo ago[dead]
- Aperocky 6mo agoOff meta: Are we tired of "one single product that solves everything" that every single AI product has became? grep didn't try to also do what awk does, and jq and curl did exactly what they needed to do without wanting to become an OS (looking at you emacs), can we have that in the AI world? I hope/think we will, in a few years, once this century's iteration of FSF catches up.
- iLoveOncall 6mo agoDeveloping and serving GenAI models is highly unprofitable, so, no, we're not going to have that in the AI world. Either those model developers & providers package them in as many services as possible so that they can be somewhat profitable, or they die, and we don't have model developers & providers anymore.
- Aperocky 6mo agoWell, the product here has nothing to do with serving GenAI models. It's now application territory. And I prefer unix philosophy vs. the Copilot product approach.
- add-sub-mul-div 6mo agoI'm tired of endless LLM spam submissions from people who only use their accounts here to advertise and self promote.
- Aperocky 6mo agoLLM submissions are no different from tech submission of yesterday. But most people used to build tools that does one thing well instead whatever the current meta is.
- nickphx 6mo agoi have found flagging and ignoring is better than complaining... the bots dislike those that point our the uselessness of llm.
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- ricktdotorg 6mo agois it just me or was the scrollbar purposefully hidden on this site? in chrome on windows, i found it very jarring and user-hostile to NOT know how far along i was in reading the article. i make a judgement call early on: is this worth my time? my whole article calculation algo was thrown off by this. do not like.
- pwr1 6mo agoWe tried something similar at a previous company — ended up with 3 different bots all answering slightly differently depending on which doc chunk they hit. The consistency problem is real. Curious how you handle updates. Like if someone edits the source doc, does the bot just start returning different answers or is there a review step?
- oliver236 6mo agodata engineering is all you need. everything else is smoke all ai applications are smoke and will be obsolete in a year do not be deceived
- tclancy 6mo agoNice to see you getting out now and then Rorschach.
- oliver236 6mo agoits crazy that people downvote me. how is what im saying wrong? are the vc's just butthurt?
- tclancy 6mo agoDo you feel like that post reads as a reasonable response that is engaging in good faith and is interested in hypothesis, antithesis, synthesis? Because as someone constantly posting in bad faith I think we are on the same team.
- fishtoaster 6mo ago> This is the part that doesn’t demo well. ETL pipelines feeding into BigQuery from every operational system: Salesforce, Zendesk, and a dozen other internal tools. dbt transformations that normalize and document the data. Column-level descriptions for every table in the warehouse, because an AI agent that doesn’t know what a column means will write SQL that looks right and returns wrong numbers. I'm glad they called this out. For the first half of this, I kept thinking: "Either your answers are confidently wrong or you've done a ton of prep work to let your AIs be effective BI analysts." Sounds like it's the latter, and they're well aware of it!
- jgalt212 6mo agoShe opens Slack, types a question to our internal agent: “Give me the names of all employees who have recently complain about my leadership” Prior having such a product it was such a chore for her to track down all the people who may have objected (dispassionately or otherwise) to my plans, strategies, objectives, etc.
- xmprt 6mo ago> When a question touches restricted data — student PII, sensitive HR information — the agent doesn’t just refuse. It explains what it can’t access and proposes a safe reformulation. "I can’t show individual student names, but here’s the same analysis using anonymized IDs." This part is scary. It implies that if I'm in a department that shouldn't have access to this data, the AI will still run the query for me and then do some post-processing to "anonymize" the data. This isn't how security is supposed to work... did we learn nothing from SQL injection?
- thunfischbrot 6mo agoIn the strongest interpretation of that it would offer only data which the user is allowed to access. Why do you assume that them implementing a feature to prevent PII being accessed that they then turn around and return data which the user is not supposed to access?
- xmprt 6mo agoIf it's PII data the best thing for them to do is not even allow the AI to have access to it. They're admitting to that so I doubt they've gone through the effort to forward the user's auth token to the downstream database. And with security it's always best to assume the worst case (unless you're certain that something is safe) because that would lead you to add more safeguards rather than less.
- adambb 6mo agoTo be fair to them, the architecture description said that each datasource had a unique agent, so the orchestrator AI didn't have direct data access, and that they specifically only allow access to data the user has permissions for. Unclear if each datasource agent is ALSO AI based though, in which case it has just pushed the same concern down the line one hop.
- stephbook 6mo agoI see two vectors here - The bot giving out PII by accident. You ignore it and report it. - You trying to fool the bot into giving you PII you're not supposed to have. But you've created an audit trail of your 100 failed prompt injections. The company fires you. This isn't public facing, open to anyone. This is more like a shared printer in the office.
- gavinray 6mo agoShameless plug: My org does this, and we deactived our Slack server to dogfood https://promptql.io https://promptql.io By building Hasura [0], we already had the ability to generate data catalogs + metadata layer from DB's + API's so the foundational infra was there [0] https://github.com/hasura/graphql-engine https://github.com/hasura/graphql-engine
- Animats 6mo agoWhy do you still need the CEO? With this, Claude should be able to do the job. Maybe have the board look at a weekly summary and tweak policy.
- themafia 6mo agoSo all your competitors can do exactly as good of a job as you. What's to differentiate your offerings from literally anything? Do you think customers are _eager_ to do business with a CEO less entity?
- cat-turner 6mo agoGreat read. shows you what is possible