We help online and physical retailers stop making decisions on gut feel. From demand forecasting that prevents overstock and stockouts to order fulfilment automation and AI customer service, we build systems that scale your operations without adding headcount.
Orders processed
Reorders raised
Stockouts prevented
Simulated inventory intelligence system. Real deployments connect to your warehouse, ERP, and sales channels.
Buying decisions are made on experience and instinct. The result is overstock on slow lines and stockouts on fast-moving products, both of which cost money and frustrate customers.
Orders from multiple channels land in different places. Staff manually check, process, and route them. As volumes grow, errors and delays increase proportionally.
Inventory across website, marketplace, and physical stores is managed in separate systems. Overselling and discrepancies are inevitable without real-time synchronisation.
High volumes of repetitive enquiries about order status, returns, and product availability consume agent time that should be spent on complex customer issues.
Processing returns manually is slow and expensive. Root-cause analysis of return patterns rarely happens, so the same problems recur without correction.
Pricing decisions are slow and rely on manual competitor monitoring. Promotional uplift is difficult to forecast accurately, leading to margin erosion or missed revenue.
Machine learning models that predict demand by product, channel, and time period. Factor in seasonality, promotions, and external signals to buy the right stock at the right time.
Order processing workflows that receive, validate, route, and confirm orders automatically across all channels. Exceptions are flagged; everything routine runs without human intervention.
Real-time inventory synchronisation across your website, marketplaces, and physical locations. Prevent overselling, reduce manual updates, and maintain accurate availability at all times.
Conversational AI that handles order status, returns, and product enquiries automatically. Integrates with your order management system to provide accurate, real-time responses around the clock.
AI that identifies return patterns by product, customer segment, and reason. Surfaces actionable insights to reduce future returns and improve product listings and fulfilment accuracy.
Automated pricing systems that adjust in response to competitor prices, demand signals, and margin targets. Make pricing decisions faster and more consistently than any manual process.
We talk through your biggest operational pain points, the channels and volumes you operate across, and what success looks like for your business. No prior AI knowledge needed.
We review your sales history, inventory data, and order management processes. We identify the highest-impact opportunities and provide an honest assessment of what your data can support.
We design the solution and integration plan. For forecasting projects, this includes model selection, training approach, and validation methodology. For automation projects, it covers workflow mapping and system connections.
We build iteratively and validate against historical data before any live deployment. For forecasting models, you see accuracy metrics against your own sales history before committing to a go-live date.
We go live with a subset of products or channels first, monitor performance closely, and expand once results are confirmed. This limits risk and gives your team time to build confidence in the new system.
AI models improve with more data. We monitor performance post-deployment, retrain models as patterns change, and expand automation into new areas as your business grows.
Accuracy depends on the quality and history of your sales data. Most retailers we work with see forecast accuracy improve significantly compared to manual methods, particularly for seasonal patterns and promotional uplift. We always validate forecasting models against historical data before deployment so you have an honest baseline before going live.
In most cases, yes. We have experience integrating with Shopify, WooCommerce, Magento, and custom platforms, as well as common warehouse management and ERP systems. We assess integration feasibility during scoping and build connections that work within your existing infrastructure.
Ideally 12 to 24 months of sales history gives a strong foundation for forecasting. However, we have worked with businesses with less data and used alternative approaches such as category-level patterns and market signals to compensate. We will be honest about what is achievable with the data you have.
AI customer service tools handle high volumes of routine enquiries automatically, which means your human agents can focus on complex or sensitive cases. Most of our clients see customer satisfaction improve because response times drop and agents handle fewer repetitive questions. Whether that affects headcount is your business decision.
Most retail and e-commerce projects deliver measurable results within 6 to 10 weeks. Order processing automation and customer service bots typically go live first. Demand forecasting and inventory optimisation models require more validation time but deliver significant ongoing savings once deployed.
Book a discovery call to discuss how AI can reduce inventory waste, automate order processing, and scale your customer service without adding headcount.