Conversational BI
If your Power BI deployment hasn't reduced the number of ad-hoc data requests landing in the analyst team's inbox, the problem isn't the tool. It's that the tool requires BI literacy that most of your users don't have. Conversational BI removes that barrier.
What we hear from operators
The problems we solve
The analyst team is buried in one-off data requests
The dashboard was supposed to enable self-service. Instead, every time a manager wants a number that isn't on the standard view, they email the analyst. The analyst pulls the data, formats it, sends it back. The manager has what they need — three days later, after the decision window has passed. Power BI is running. Self-service analytics is not.
Dashboard adoption stops at the dashboard
Adoption metrics for most Power BI deployments look good at launch and drop sharply within three months. The users who persist are the ones comfortable with data visualisation. Everyone else reverts to spreadsheets and email chains. The problem is the interface — charts and filters require a mental model that most business users haven't been trained to use.
The semantic model exists but only three people know how to query it
After a Power BI implementation, most organisations have a well-structured semantic model with clean measures, calculated columns, and a proper dimensional model. And usually two or three people who can use it effectively. The investment in the data model is underutilised because the access point — the Power BI front end — is too narrow.
By market
Conversational BI — market-specific pages
Each page below covers what conversational bi looks like specifically in that market — the local ERP landscape, compliance context, and the operational patterns we actually see there.
Singapore & Malaysia
United Kingdom
North America
By industry
Conversational BI — industry-specific pages
How conversational bi applies to the specific systems, metrics, and operational challenges of each vertical.
Manufacturing
Most manufacturing plants we walk into have four or five systems that don't talk to each other: SAP or Oracle for production orders, a separate MES for floor execution, a quality system that's often standalone, and spreadsheets filling every gap in between.
Explore →
FMCG & Retail
FMCG and retail data problems concentrate at two points: the demand signal and the shelf.
Explore →
Packaging
Packaging plants sit at the intersection of manufacturing analytics complexity and FMCG demand volatility.
Explore →
Logistics & Supply Chain
Logistics operations in the GCC, India, and Southeast Asia share a common data challenge: high transactional volume, multi-party execution (3PL, 4PL, last-mile carriers), and a fragmented visibility picture.
Explore →
Technology stack
Start with a conversation, not a proposal
First call is 45 minutes. No deck. We ask about your systems, your team, and your most pressing operational problem. You get a clear view of where the gap is and what closing it looks like.