Retail's answer for the data layer

Ekyam is an AI-native data platform for retail. It standardizes and contextualizes SKU-level information across ERP, OMS, WMS, POS, PIM, commerce platforms, data lakes, and flat files, creating one shared, governed view of inventory. That reconciled data layer is what lets retailers operate on data they trust today. It is also what determines whether their AI initiatives will work at scale tomorrow.

Two layers make this possible, and each stands on its own.

The integration layer is a retail-specific iPaaS that uses AI to standardize data across workflows via a proprietary canonical model. It connects source systems through roughly 100 pre-built connectors, features no-code, bi-directional mapping and seamless, robust orchestration capabilities.  Integrations with Ekyam are completed nearly four times faster than conventional integration tools and deliver real-time data flows between systems.  A global footwear retailer used Ekyam to integrate legacy systems across 23 countries, moving store-to-ERP sync from daily batch to every three minutes and cutting time to close the books in half. A specialty jewelry retailer integrated five core systems and reduced store-level inventory variance from nearly 20% to under 6%.  For SIs building composable stacks, this compresses the integration workstream so they can focus where they add the most value: the applications and agents their clients need.

Their semantic layer is a Retail Knowledge Graph which encodes a SKU-centric ontology.  The graph and ontology map how SKUs relate to customers, orders, locations, and channels.  Ekyam also records how each record changes over time, providing governance and metric lineage. The semantic layer can sit over a retailer's existing systems and data warehouse or lake and allows their existing tech stack, even legacy, to be used for agentic applications. For SIs building agents and ISVs operating at the application layer, this means the inventory data feeding their tools is accurate, consistent, and current.

What the two layers solve together is what has historically been the largest source of inventory distortion and is now the largest obstacle to enterprise-wide AI: data that sits fragmented across systems, defined differently in each, and never resolved. 

Reconciled, contextualized data is the prerequisite for AI readiness. Hand an LLM or workflow agent raw exports from several systems and it faces conflicting values, re-derives every metric, guesses at definitions. Different runs produce different answers on the same data. Point that same agent at Ekyam and it reads one pre-resolved record per SKU with metrics already computed and full history attached. Same question, same answer, every time, at a fraction of the token cost. Agents do not fail because models are not good enough. They fail because the data underneath them is not ready.

Ekyam joined the MACH Alliance because its architecture is built on the same principles. It is open: the knowledge graph runs on Neo4j, queried in Cypher, with agents connected through the Model Context Protocol and every skill exposed as a REST endpoint. It is composable: four independently deployable layers a retailer can adopt incrementally or swap without disturbing the rest of the stack. And it is connected: relationships are modeled as data in the graph, so cross-domain questions resolve as traversals rather than joins.

Ekyam is SOC 2 certified with GDPR and ISO 27001 aligned architecture.

For more information on MACH at Ekyam, please contact the team.