Querri
Question Anything
742 followers
Question Anything
742 followers
Querri brings your company's data together across 100+ platforms, giving every department a shared foundation for reliable, repeatable answers. Connect, clean, and combine your data, then teach Querri the definitions and context that matter to your business. Ask questions in plain English, build dashboards and presentations, automate recurring analysis, or work with your data directly from Claude and ChatGPT.
This is the 3rd launch from Querri. View more
The Library
Launching today
The Querri Library is where your data gets organized around the questions your business asks, so every answer comes from the same trusted, shared foundation.. The library learns your definitions, business context, and past analysis, so its answers are grounded in how your business actually works. And when it doesn’t know, it says so, instead of making things up.
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@annachianna great questions! This is definitely all collaborative. We built permissions and workspaces in Querri to mirror the shape of real organizations which are rarely super clean. The definitions themselves get written with the Librarian agent's help, so if your operator describes something that doesn't exactly match the data, it can assist in getting that right and ask for clarification. That said, for companies that have the luxury of a data team, we see the data team also as admins within the system and able to check in on the SQL being written behind the scenes and help troubleshoot and improve as the business iterates on their dashboards and agent interactions.
The other big thing we do is have an agent run on a nightly basis and look at the questions other people are asking to surface the "hard" ones (for example involving several agent turns or involving complex joins) and then the workspace owner can review and help to clean it up.
Anyway, data is messy and probably always will be, but we've found a lot of ways to give tools to both the technical user, as well as the business user who is often in the best place to know if the numbers are actually correct or not.
Dave and the team have been amazing to work with as I've looked to roll out better visibility across a number of systems across multiple companies in our portfolio. There has been no system they've been unwilling to try and build connectors for which has been a huge time benefit for me in a job where time is a premium.
@neil_amin Thank you so much! It means a lot to hear this. Our team is always willing to tackle challenges and find solutions, even when they're not straightforward. We know your time is valuable, and we're excited about the opportunity to make your job a little easier. Really appreciate your trust and support!
@neil_amin Thanks Neil - you and your team have been a pleasure to work with. It's been great enabling you and your firm to see data across many systems and also being able to give consistent views of data for each of your portfolio companies.
Congrats Dave, Amy and the Querri team.
The point about the same question getting a different answer tomorrow is the real problem with AI on top of data.
When two teams define "active customer" differently, does the Library force one definition, or can both live side by side?
@genedai Thank you, Gene! And that's a great question.
Both definitions can be valid! What Sales considers an "active customer" might differ significantly from how Customer Success defines it.
The goal isn't to force everyone to agree on one definition if there are legitimate differences. It's to make those differences explicit, with clearly defined metrics, so the AI isn't guessing which one to use.
Ultimately, we want people to understand not just what the number is, but how it was calculated and what it actually means.
That's exactly the kind of nuance we've been working so hard to address!
@genedai great question! ... Querri has "workspaces", and those are connected, so if you have a genuine need to track two different definitions you can put the global one in the shared workspace and override it in another. But inside of one workspace, our "Librarian" agent always looks at what already exists before creating new KPIs, business rules, Views etc, so if you try to create a duplicate rule it's going to ask you if you want to update or explicitly create something with a new definition. And all of this stuff can have "facts" hanging off of it, so you can clearly tell the AI about each definition and then when you ask about "active customers" going forward it would prompt you with something like "did you mean 'active customers' or 'active paying customers'?" .. or whatever the distinction is.
Congrats on the launch! Really like that it says what's missing instead of giving a confident wrong number.
@byalexai Thanks! We spent a lot of time on that .. it's a lot harder than it feels like it should be!
Saving what the team means by “sales” is the detail that stood out. It gives people something concrete to check when two reports disagree.
@oleksii_sekundant it's made a huge difference to both accuracy and usability of the whole platform. You can't run a business on AI without trust!
Really like this approach. Building AI around a business’s actual context rather than just giving it access to data feels like a meaningful step forward. Good stuff!
@stephen_doyin yes, that's what separates Querri from other data tools because now Querri can give accurate and relevant responses as opposed to spitting out numbers that dont make sense.