Retail data is fragmented across dozens of systems. Ekyam is the semantic layer that unifies it — merging a Knowledge Graph, Vector Database, and Document Store into a single governed platform that gives every team one trusted language for the business.

Knowledge Graph

Entity relationships + Ontology + Inference

Canonical retail entities (Product, SKU, Customer, Store, Vendor, Order) are nodes. Ontology edges encode business meaning — Product → hasSKU → SKU, Customer → purchased → Order. Inference rules derive new facts: if a customer bought in-store and online, they're omnichannel.

Vector Database

Embeddings + Semantic search + Similarity

Converts unstructured data — product descriptions, customer reviews, images, support tickets — into high-dimensional embeddings. Enables semantic similarity search (“find products like this”) and powers AI/RAG pipelines with contextual retrieval from the knowledge graph (GraphRAG).

1. KG identifies relevant entity subgraph (e.g., all SKUs in "Men's Footwear").
2. Vector DB retrieves semantically similar items within that scoped context.
3. Results combine structural precision (graph) with semantic richness (embeddings).
4. Answers are explainable — path from entity to result is visible.

MongoDB — Document Store

Structured + Unstructured canonical records

The persistent store for canonical entity documents. Flexible schema handles structured data (pricing, inventory counts) alongside semi-structured / unstructured data (variant attributes, rich descriptions, images) — no forced normalization.

Caching Layer

Redis + Materialized views + Semantic cache

Multi-tier caching accelerates every query path. Materialized aggregate views serve common dashboard queries instantly. Redis semantic cache (LangCache) deduplicates AI/LLM calls by matching query meaning, not just exact text. Event-driven invalidation keeps caches fresh.