Ask questions of your data in plain English. AI that analyses spreadsheets, databases, and reports without requiring SQL or specialist skills.
Data holds answers your business needs. But getting those answers requires technical skills, available analysts, and time. AI-powered analysis changes this equation, letting business users ask questions in plain language and get answers from their data.
Most organisations face constraints on data analysis:
Technical barriers: Querying data requires SQL, Python, or specialist tools most business users do not have.
Analyst capacity: Data teams are overwhelmed with requests. Questions queue for days or weeks.
Static reports: Pre-built dashboards answer yesterday's questions but not today's.
Interpretation gaps: Raw data reaches decision-makers who need insight, not numbers.
AI addresses each of these constraints.
Modern AI transforms data access:
Natural language queries: Ask questions like "What were sales by region last quarter?" and get answers.
Automated exploration: AI identifies patterns, trends, and anomalies without being asked.
Plain language insight: Results explained in terms business users understand.
Visualisation generation: Charts and graphs created automatically to illustrate findings.
Interactive refinement: Follow-up questions to explore data further.
AI analysis serves different analytical needs:
Ad hoc questions: One-off enquiries that do not justify building a dashboard.
Report generation: Automated creation of regular reports with narrative summaries.
Trend detection: Identifying patterns and changes that deserve attention.
Performance analysis: Understanding what is driving metrics up or down.
Forecasting: Projecting future values based on historical patterns.
AI analysis combines several capabilities:
Data connection: Accessing databases, spreadsheets, and business systems.
Query translation: Converting natural language questions into database queries.
Execution: Running analysis against actual data.
Interpretation: Explaining results in understandable terms.
Visualisation: Creating charts that communicate findings clearly.
AI analysis can work with:
Relational databases. SQL Server, PostgreSQL, MySQL.
Warehouses and lakes. Snowflake, BigQuery, Redshift.
Spreadsheets and files. CSVs and other structured exports.
Business applications. CRM, ERP, and finance systems.
APIs and feeds. Real-time sources where appropriate.
We connect to your existing data infrastructure.
AI analysis requires appropriate controls:
Data access: Ensuring users only query data they are authorised to see.
Query validation: Checking that generated queries are correct before execution.
Result verification: Confirming analysis accuracy, especially for consequential decisions.
Audit trails: Recording what was asked and what was returned.
Human oversight: Maintaining appropriate review for important analysis.
Organisations using AI analysis see:
Faster answers: Questions answered in seconds rather than days.
Broader access: More people able to get insights from data.
Analyst efficiency: Data teams focus on complex work, not routine queries.
Better decisions: Relevant data available when decisions are made.
Discovery: Insights found that would not have been sought manually.
We build AI analysis appropriate to your data environment:
Data assessment: Understanding your sources, structure, and quality.
Connection setup: Establishing secure access to relevant data.
Model configuration: Training AI on your data schema and business terminology.
Interface development: Creating appropriate ways for users to interact.
Testing: Verifying accuracy across representative queries.
Deployment: Launching with appropriate training and governance.
AI analysis succeeds when safety and correctness are designed in.
Separate exploration from decisioning. “Research mode” can be more flexible; “reporting mode” should be validated and repeatable.
Validate queries and results. Generated SQL should be checked for correctness and safety, especially when data is sensitive or the query is complex.
Define a glossary and semantic layer. Clear definitions prevent “metric drift” where different teams interpret results differently.
Use these prompts to define a reliable analysis experience.
“What are the top 20 questions our teams ask repeatedly, and what data sources answer them?”
“What validation rules do we need to prevent incorrect queries, unsafe joins, or misleading aggregations?”
“What audit trail should we keep: questions asked, queries executed, and results returned?”
No. It reduces time spent on routine queries so analysts can focus on complex work, modelling, and governance.
Query validation, restricted schemas, fixed metric definitions, sampling and review, and clear fallbacks when confidence is low.
Yes. Access control and row-level security patterns still apply; the interface should respect them.
It can, but data quality limits insight quality. We often start with a small set of trusted tables and expand.
Faster time-to-answer, broader self-serve access, fewer routine analyst tickets, and consistent metrics people trust.