Business Intelligence AI

In most cases it works alongside your existing BI tool and data warehouse rather than replacing it — we build the AI layer to query your current metric definitions and data sources so the answers stay consistent with reports your team already trusts. Replacing the underlying BI stack is only necessary if what you have genuinely can't support the integration, which is rare.

We ground every answer in your actual data and metric definitions rather than letting the model reason freely, and we build in explicit guardrails so it flags uncertainty or insufficient data instead of guessing. We also test it against known-answer questions from your team before rollout to catch mistakes before your stakeholders see them.

Typically it's the non-technical stakeholders — leadership, sales, ops managers — who currently wait on the analytics team for a custom chart or report; they get to ask questions directly and get answers grounded in real numbers. Your analytics team still owns the underlying data model and metric definitions, they just spend less time building one-off reports.

Timeline depends mostly on how clean and well-documented your existing metric definitions are — a data warehouse with clear, consistent definitions can be integrated in a few weeks, while messier setups need cleanup work first. We start with an assessment of your current BI setup so the timeline estimate reflects your actual starting point, not a generic average.