How is this different from a BI tool like Looker or Tableau?
Those tools are generic platforms you configure yourself. We build the dashboard and its logic specifically around your metrics and add a plain-language query layer on top.
Can we ask it questions directly?
Yes — the AI layer is designed to answer follow-up questions grounded in your actual data, not a generic sample.
What data sources can it connect to?
Most databases, warehouses, and APIs — we scope the specific connections during discovery.
Will it flag things automatically, or do we have to look?
We can build in anomaly detection so unusual patterns surface proactively, not just on request.
Do we need a data team to maintain it?
No — it's built as production software you own, with documentation, not a tool requiring a dedicated analyst to run.
How does it know what's actually unusual for us?
We tune anomaly detection against your real historical data rather than using a generic statistical threshold that doesn't account for your normal seasonality.
Can non-technical team members use it?
Yes — the plain-language query layer is built specifically so people who don't write SQL can ask real questions and get a grounded answer, not a dashboard they need an analyst to interpret.
Can it combine data from more than one source?
Yes — a common reason teams outgrow off-the-shelf BI tools is needing one view that blends data from a CRM, a billing system, and internal operations data, which we build as a first-class part of the data layer, not a workaround.
What if we need a new metric six months after launch?
Adding a metric is a normal development request against your own codebase, not a wait for a vendor's roadmap or a workaround with pivot tables.
Can it alert us somewhere other than the dashboard itself, like Slack or email?
Yes — for anomalies or thresholds that need immediate attention, we can route alerts to Slack, email, or another tool your team already watches, rather than relying on someone remembering to check a screen.