Retail
AI-assisted analytics and reporting for a retailer
Weekly reports turned daily, and deeper analysis opened up to business teams — without sensitive data leaving the company.
Proves · Applied AI + Data + Automation
- Daily
- reports (was weekly)
- In-house
- AI analysis of sensitive data
Challenge
Reporting was manual and slow: teams across the business received key reports about once a week, and deeper analysis such as customer segmentation depended on a scarce data specialist. AI was tempting, but sending customer and sales data to external AI tools was not acceptable.
Approach
The work started where AI projects usually fail — the data. After cleaning and modelling it into a reliable foundation, the AI was given the data structure (schema), never the data itself. From the schema it writes the code that answers a business question and returns what is needed: a spreadsheet extract, a customer list, a dashboard or a report. A model running inside the company handles the first pass over real data, so nothing sensitive leaves. Reports are then generated and delivered on a schedule.
Outcome
Reporting moved from weekly to daily, with fewer errors from manual handling. Analysts stopped rebuilding reports and moved to deeper work, and analyses that once needed a data specialist — segmentation, clustering, product association — became something business teams could run themselves.
Private by design
Cloud AI only ever sees the data structure; real data is analysed by a model running in-house.
Clean data first
The longest and most important step. AI is only as good as the data under it.
Adoption, not just a launch
Tiered training, a super user in each team, and written procedures for first-line support — so the tools were still in use months later.