Demand insight
Explore sales patterns and forecast activity to support stock decisions.
Retail & e-commerce
Understand demand. Make daily work easier.
Use sales, product and service information to support planning, relevant recommendations and more consistent customer assistance.
Useful starting points
From an operational need
to a focused project.
Illustrative applications to weigh against your own processes and requirements. Each one is a possible entry point that we would shape around the way you work.
Product knowledge assistants
Help a service team find current, approved product information.
Business reporting
Connect sales and operational measures without losing the definitions behind them.
Designing for your context
The details matter.
Customer information, recommendation quality and changing demand require ongoing review. Examples should be tested against the actual product range and business workflow.
What we would establish together
- The task and the people responsible for it
A clear picture of what changes, who is affected and what a good outcome feels like to the people doing the work.
- Source systems, information quality and access
Where the data comes from, what state it is in and what permissions are required to work with it responsibly.
- Operational constraints and review requirements
What cannot change, what must go through approval and what the consequences of an error would be.
- An initial scope and a way to assess the result
A defined first phase with agreed success criteria so the work can be evaluated before a larger commitment is made.
Common questions
Retail & e-commerce projects,
answered plainly.
Short answers to the questions that usually come up before a project in this sector begins.
It can, once it has proved reliable with your service team. We usually start with an internal assistant whose answers staff check, then widen its use based on test results.
There is no fixed minimum. Seasonality, promotions and new launches all matter. A review of your records shows whether forecasting is realistic and at what level of detail, such as by store, category or week.
Sometimes simple rules, such as items often bought together, perform as well as a model. We compare the options on your own data before suggesting anything more complex.
Relevant expertise
Services for this context.
Machine learning
Use your historical data to build models for prediction, classification and recommendations, with evaluation that reflects how the model will be used.
Explore serviceGenerative AI & LLMs
Build assistants, copilots and language model applications that help people find information, draft work and use approved business knowledge.
Explore serviceData analytics & BI
Bring scattered business data into a clearer picture, with consistent measures, useful dashboards and reporting built around the decisions you make.
Explore serviceStart a conversation
What would you like to build?
Tell us what is slowing you down, or what you want to do next. A short description of your business question is all it takes to begin.