Retail Pipelines
Drag, deploy, done. Each pipeline runs on our in-memory DAG engine with sub-10ms latency.
Ingest raw product feeds, enrich descriptions with AI-generated SEO copy, extract structured attributes, and write enriched catalog data to your store. Handles thousands of SKUs per minute.
Stream social mentions from Kafka, classify sentiment with AI, aggregate scores per product/brand over tumbling windows, and route negative spikes to alert sinks. Real-time brand health at a glance.
Monitor inventory change events in real time. Detect sudden stock drops, unusual reorder patterns, and potential stockout risks using rolling aggregation and threshold-based AI analysis. Alert operations teams before customers see "out of stock".
An autonomous AI agent that takes a customer's browsing history and purchase data, reasons over your product catalog using a ReAct loop, and generates hyper-personalised product recommendations. The agent uses tool calls to query inventory, check pricing, and cross-reference reviews before composing its final recommendation set.
The LLM Agent autonomously decides which tools to call (inventory lookup, price check, review search) and iterates through Thought → Action → Observation cycles until it has enough context to generate quality recommendations. No hardcoded logic required.
See these pipelines running live with your product data. Book a 30-minute demo with our team.