Swiftron Unlock Your Full Potential with AI.
Anticipate missing basket items in milliseconds. We provide a custom Transformer model trained on your raw ERP data to translate historical buying patterns into highly accurate product predictions, significantly increasing your Average Order Value.
A digital B2B purchase is almost always abandoned if the shopping cart feels incomplete to the customer. Whether a highly needed complementary part cannot be found or is currently out of stock—the consequence is an abrupt cart abandonment. Industry studies confirm that around 70 percent of B2B shopping carts fail due to exactly this frustration.
This is where our Transformer steps in. Instead of leaving the buyer to manually search for additions or substitutes, our model anticipates the missing items in real-time. Based on your historical orders, the AI proactively fills these gaps and immediately gives the customer the feeling of a complete, logical shopping cart before they switch to a competitor.
Experience the power of autoregressive inference in action. In the simulation below, you can interactively observe how our Transformer model decodes an active order pattern. The moment you place an item into the virtual basket, the model calculates the logically next product based on millions of historical transaction sequences. The demo illustrates how the AI anticipates missing components or suggests fitting substitutes to complete the basket in a fraction of a second.
Our Transformer model learns directly from the raw order history of your ERP. It captures the actual, proven buying patterns of your entire customer base. The key to our unprecedented sub-second performance lies in how this knowledge is utilized. Instead of executing slow, synchronous search queries against a massive ERP database during checkout, all commercial rules and purchasing behaviors have already been transferred into the AI model's parameters while training.
Because the causal logic is pre-compiled into the neural network, the AI instantly recognizes that adding items A and B typically requires item C for completion. By decoupling the heavy data processing from the live environment, we eliminate latency bottlenecks while delivering predictive cross-selling that provably increases the Average Order Value by 20 to 35 percent.
We provide the pure algorithmic power of our foundation model, empowering your trusted IT ecosystem to handle the implementation. Our predictive engine is licensed and delivered as a secure, containerized Docker image. This ensures your sensitive B2B order data never leaves your controlled environment. Certified SAP consultants, system integrators, or your in-house development team take this container and deploy it directly within your own infrastructure.
By exposing a clean REST or gRPC interface, we allow your partners to seamlessly connect the AI logic to your specific frontend and backend systems. They build, customize, and operate the final e-commerce workflow, guaranteeing that our predictive intelligence blends flawlessly into your corporate architecture.
Your data remains sovereign. The Swiftron model is deployed as an isolated, containerized Docker image directly within your own or your trusted partner's IT infrastructure. We do not extract or process your raw B2B data on external cloud servers.
No. The computationally heavy pattern recognition occurs asynchronously during training. Live inference utilizes a ultra fast prediction, delivering precise recommendations via REST or gRPC APIs in milliseconds, completely bypassing slow, synchronous ERP database queries.
Yes. Traditional recommendation plugins fail here, but our Causal Transformer is designed specifically for sparse B2B environments. It uses ALiT embeddings to map the geometric relationships between products across your entire customer base, identifying underlying procurement patterns even with low purchase frequencies.
We solve this "Cold Start" problem using Point Cloud Alignment. Newly introduced items are mathematically positioned in the latent vector space near similar products. This allows the AI to recommend them accurately from appearing in only little orders, without waiting months for historical data.
Not at all. Swiftron provides the predictive foundation model as an API-first engine. Your existing IT system house, SAP consultant, or agency handles the frontend integration, using the API to build a customized e-commerce workflow that fits your specific needs.
The model trains on raw, anonymized transaction sequences. Because the Transformer architecture learns causality directly from sequence data, no complex manual data tagging or extensive pre-processing is required from your side. A simple CSV Export is sufficient.
Leverage the hidden potential of your raw ERP transaction data to prevent expensive cart abandonments and automatically maximize your order value. We gladly evaluate the structure of your data foundation and subsequently connect you with certified IT partners who will integrate our high-performance model into your individual system architecture.