Conversational BI
Plant managers, operations supervisors, and supply chain planners should not need BI training to get answers from your data platform. Conversational BI puts a natural language interface in front of your Power BI semantic model — ask a question in plain English, get the answer from your data. Deployed on Microsoft Teams or as a web interface. Built on Azure OpenAI and Copilot Studio.
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.
Plant floor supervisors cannot use a BI tool during a shift
A shift supervisor on a production floor does not have time to open Power BI, navigate to the right report, apply the right filters, and interpret the chart. They need to ask "what was our line 3 OEE last shift and what caused the biggest downtime event?" and get the answer in 10 seconds. Conversational BI is the interface that makes analytics accessible to the people closest to the operation.
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 have not 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 for the operational audience.
Who this is for
Who conversational bi is built for
The roles that feel the problem first — and what we build for each of them.
Operations Director
The problem
Power BI is deployed, but 80% of the organisation looked at it once and went back to emailing the analyst for every number that is not on the standard view.
What we build
A natural-language layer over your Power BI semantic model using Azure OpenAI and Copilot Studio, deployed in Microsoft Teams.
40–60% fewer ad-hoc report requests
IT Head
The problem
The analyst team is buried in one-off data pulls, and the backlog grows faster than they can clear it.
What we build
A Copilot Studio interface that honours the same row-level security as Power BI, so managers self-serve without adding to the queue.
Analyst backlog shrinks as self-service replaces the email queue
Head of Data
The problem
You have a well-built semantic model with clean measures, and two or three people who can actually query it — the investment is underused.
What we build
An Azure OpenAI conversational layer trained on your measures and terminology over Microsoft Fabric, opening the model to the whole operation.
Semantic model reaches the operation, not three power users
Shift Operations Manager
The problem
On the floor you need last shift's line 3 OEE and the biggest downtime cause in ten seconds — there is no time to open Power BI, filter and read a chart mid-shift.
What we build
Plain-English answers from the data in Microsoft Teams via Copilot Studio, with the supporting chart attached.
Answers in the moment, not three days after the shift
Measurable outcomes
What changes after implementation
Specific shifts from delivered conversational bi work — the before, and the after.
Ad-hoc data requests to analyst team: reduced by 40–60% within 90 days
Users who previously emailed the analyst team start getting answers from the conversational interface. The analyst team's backlog shrinks and their time shifts to higher-value work.
Power BI active users: typically 2x within 6 months of Conversational BI deployment
Users who never used Power BI directly engage through the conversational interface. Active user counts — the real measure of analytics adoption — double as the barrier to access is removed.
Decision latency: 3-day wait for analyst → instant answer from the data
Managers get answers to data questions in the meeting, not three days after it. The quality of decisions improves when the data arrives before the window closes.
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.
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FMCG & Retail
FMCG and retail data problems concentrate at two points: the demand signal and the shelf.
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Packaging
Packaging plants sit at the intersection of manufacturing analytics complexity and FMCG demand volatility.
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Logistics & Supply Chain
Logistics operations in India, the GCC, and Southeast Asia share a common data challenge: high transactional volume, multi-party execution (3PL, 4PL, last-mile carriers), and a fragmented visibility picture.
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Technology stack
Start with a conversation, not a proposal
First call is 30 minutes with Amit. 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. No slides. No pitch deck. No obligation to proceed.