Natural-language reporting questions
Let users ask supported questions about approved measures and dimensions. Show the interpretation, period and filters so they can check whether the system answered the question they intended.
Data & decision intelligence
AI business intelligence
Make business reporting more accessible through connected data and AI-assisted exploration.
Built around a specific business need.
Business intelligence becomes frustrating when users know the question they want to ask but cannot find the right report or interpret conflicting measures. Natural-language interfaces can make exploration easier, yet they can also produce confident answers built on the wrong definition, filter or level of access.
We combine dependable reporting foundations with controlled AI-assisted analysis. Agreed business measures and authorised datasets form the starting point. The interface helps users ask questions, inspect the reasoning and trace results back to data, while making uncertainty and unavailable information clear.
Who this is for
- Business teams seeking easier access to trusted reporting.
- Analysts supporting repeated questions across several departments.
- Leaders who need explanations alongside management measures and trends.
Where this can make a practical difference.
Variance explanation assistance
Surface contributing categories and relevant changes behind a movement in performance. Separate observed associations from causal explanations and link any narrative to the underlying figures.
Analyst workbench support
Assist analysts with query drafts, measure documentation or report summaries. Keep reviewable intermediate outputs and protect the distinction between exploratory analysis and published organisational reporting.
How we approach the work.
Define the business vocabulary
Agree measures, dimensions and common terms with their owners. Resolve ambiguous language such as active customer, conversion or completed case before adding a conversational interface.
Prepare governed reporting data
Create appropriately scoped datasets and stable calculations. Apply access restrictions in the data and application layers rather than expecting the language model to enforce permissions.
Design transparent question handling
Ask clarifying questions where interpretations differ. Display selected measures, filters and time periods, with a clear route to the underlying table or chart.
Constrain analytical execution
Use approved query patterns or validated read-only access appropriate to the design. Bound query cost and output size, and reject unsupported operations or requests outside the user’s authority.
Evaluate real business questions
Test answer correctness, ambiguity handling and permissions against reviewed examples. Include missing data and misleading questions before opening the interface to a wider audience.
The sequence is tailored to the agreed scope, dependencies and risk. It describes our delivery approach, not a fixed timeline or performance guarantee.
What the engagement can deliver.
- Business glossary and agreed reporting measures.
- Authorised analytical datasets and query controls.
- AI-assisted question and explanation interface.
- Source-linked answers and interpretation displays.
- Evaluation examples and reporting ownership guidance.
The proposal identifies the deliverables, acceptance evidence, responsibilities and support arrangements included in your engagement.
Measure what matters to this workflow.
Answer correctness
Compare results with reviewed answers, including the interpretation of scope, filters and reporting period.
Clarification quality
Assess whether ambiguous requests are resolved before the system presents an answer as definitive.
Analytical task time
Measure the effort needed to obtain and verify an answer, including any analyst correction.
These are measures to discuss and baseline. Targets are agreed for the specific system and data; no universal improvement percentage is assumed.
What to prepare for discovery.
- A set of recurring reporting questions and their accepted answers.
- Named owners for measures and analytical datasets.
- Clear permissions for detailed, aggregate and sensitive information.
Questions worth asking.
Will users be able to ask anything about the business?
The useful scope depends on available data, agreed definitions and permissions. We make those boundaries visible rather than implying that fluent conversation provides access to every organisational fact.
Does this replace dashboards?
It complements them. Stable recurring decisions often benefit from a designed dashboard, while a conversational interface can help users investigate additional supported questions and understand a result.
Can AI write SQL directly against production?
The design should limit authority and resource use. We assess safer reporting datasets, approved patterns and query validation before considering open-ended generation, with permissions enforced outside the model.