Retail and commerce
Recommendations, forecasting and retail intelligence
Real-time recommendation engines, demand forecasting and multi-agent question answering over retail knowledge graphs, for retailers, publishers and consumer platforms.
Before you hire an AI partner
The questions we expect, answered up front
Retail groups, publishers, brands and consumer platforms.
- Will it pay for itself?
- We agree the business metric first, such as click-through or forecast error, and measure against the current baseline.
- Can we control model costs?
- Yes. For one publisher, a self-hosted model avoided roughly $10,000 a month in API fees.
What we build here
What do we build for retail and commerce teams?
Recommendation engines, forecasting and multi-agent retail intelligence.
- Real-time recommendations with two-tower models
- Conversational recommenders that explain their choices
- Demand forecasting, pricing and inventory intelligence
- Multi-agent question answering and forecasting on a knowledge graph
- Customer data unification and segmentation
Proof
Retail and commerce case studies
Book recommendations at scale, with a self-hosted LLM
A hybrid recommendation engine over more than 10 million titles and a goal-based recommendation chatbot on a self-hosted Llama 3 model.
Result: 60% higher click-through on recommendations
Read the case studyMulti-agent retail intelligence on a knowledge graph
A multi-agent system that answers business questions, forecasts and draws its own charts by reasoning over a retail property graph.
Result: 4 specialized agents over one governed graph
Read the case studyServices
Which of our services fit retail and commerce teams?
- Core Machine learning and forecasting Forecasting, risk, churn, anomaly detection and recommendation models, shipped with monitoring.
- Core Agentic AI Planner and worker agents with typed tools, bounded retries, cost ceilings and full tracing.
- Core Analytics and BI Dashboards and semantic layers in Power BI, Qlik, Tableau and Looker, with row-level security.
FAQ
Retail and commerce: common questions
What results have your recommendation systems delivered?
For a US publishing group: 60 percent higher click-through on recommendations across more than 10 million normalized book records.
Will it work with our existing store and data?
Yes. We connect to the sources you already run, such as your product catalog, order history, warehouse and analytics, and serve recommendations or forecasts through an API your site, app or team can use.
Do we need a lot of data to get started?
Not necessarily. The assessment checks whether your catalog, transaction and behavior data can support the use case, and recommends the simplest approach that will move the metric before anything larger is built.
How is our customer data protected?
Personal data is identified and minimized in the first phase, access is least-privilege, and your data is used only for your engagement. Systems are designed with GDPR in mind and can run entirely inside your own cloud account.
How do we start, and how long does it take?
With a two to three week fixed-scope assessment that tests one real use case on your data and ends with a written architecture, costed roadmap and a go or no-go recommendation. Build timelines are committed after the assessment, not before.
Start with a two to three week assessment
Send us the problem and the constraints. You get a written architecture, a risk register and a costed roadmap, and you keep them whatever you decide next.