Every data question in the business ended up at the finance department, which had effectively become a data help desk. An intelligent Q&A agent lets business users ask in plain language and get charted answers in seconds.
A leading listed Chinese chip company whose business data is spread across ERP, CRM and various document systems, with most business data analysis long concentrated in the finance department.
Finance was the only department that held both the metric definitions and the ability to query for them. So every business question that needed data ended up as a message to finance.
The volume turned finance into a data output help desk. Its people's time went to repetitive extraction, and the large majority of those requests needed no financial judgement at all.
For the business the cost was waiting. A hypothesis that could have been checked on the spot instead joined a queue for a day or several, and by the time the answer came back the conversation had usually moved on.
The subtler cost was that follow-up questions stopped happening. When each query means imposing on someone, people ask only their single most important question rather than following the answer further — and insight tends to live in the follow-up.
The data itself was also scattered across ERP, CRM and shared drives with inconsistent definitions: the same metric could be computed differently in different systems, which made opening up self-service risky even where it was technically possible.
The company deployed an intelligent data Q&A agent. Business users ask in everyday language; the system interprets intent, runs the query, and returns charts and insight.
A semantic layer settles the definitions first, breaking down the walls between ERP, CRM and document systems and fusing structured with unstructured data into one business view. Metric logic is fixed at this layer, so everyone's question resolves against the same definition.
The Q&A layer supports multi-turn dialogue with context, recognises internal business terminology and vague phrasing, translates natural language into high-precision SQL, and recommends and generates the appropriate visualization.
Above basic Q&A sits analysis: automatic drill-down and attribution, trend forecasting and anomaly detection, and generated insight summaries — covering more than 50 common advanced financial analysis topics, so the system answers not only what the number is but why.
Query response falls under a minute, so business users get data while the discussion is still happening rather than after it.
AI absorbs 90% of the repetitive extraction work, returning the finance department to financial analysis.
Unified data management and metric definitions reach full coverage — the same metric returns the same number in any system, for anyone.
With more than 50 advanced financial analysis topics covered and the cost of a follow-up question close to zero, the business genuinely started using data to explore rather than only to report.