Product catalog, supplier, inventory, and customer-behavior data live across separate systems, with the same product, supplier, or customer recorded differently in each. LangOptima ingests the commerce data you already hold, resolves the duplicates into single entities, and connects it into a single queryable knowledge base, so teams answer catalog and merchandising questions in seconds, each result cited back to its source.
Built on the open product-data standards schema.org and the GS1 Web Vocabulary, published as RDF, so products, offers, and attributes connect across catalogs and search engines.
A product, a supplier, or a customer appears differently in the catalog, the resource-planning system, and the storefront. In many retailers, reporting and merchandising work around the mismatch rather than trusting it.
A product, a supplier, or a customer appears under different records across the catalog, the resource-planning system, and the storefront. Reporting works around the mismatch instead of resolving it.
“Which suppliers does this category depend on, and what else do they supply?” spans catalog, procurement, and inventory. Answering it becomes a data-pull project rather than a query.
The relationships between products, purchases, and customers hold the merchandising signal, but they sit in separate systems, so cross-sell and substitution logic stays coarse.
LangOptima ingests the commerce data your organization already holds, including product catalog, supplier and procurement records, inventory, and customer-behavior data, and structures what is inside them: products, categories, suppliers, customers, and the relationships between them. It resolves the duplicate and variant records of the same product, supplier, or customer into single entities, and sits above your catalog, resource-planning, and storefront systems without replacing them, so a question that once meant a data-pull becomes a single query with the source attached.
Modern AI can structure a great deal on its own. Where it stops is the meaning specific to your organization: the concepts, rules, and relationships that make your business yours, and where its real value lives. We structure that layer with you on open, world-standard semantics, not a proprietary schema, so the graph reflects how you operate and stays yours: no vendor lock-in, portable to whatever you run next. More on the structure beneath it →
Catalog, supplier, inventory, and behavior data unified at the semantic layer, duplicates resolved into single entities. Nothing moves. Nothing is replaced.
Traverse from a product to every supplier and substitute behind it, from a customer segment to the products they connect, from a supplier to everything at risk if they fail.
“What connects to this category, and what depends on it?” answered in seconds, with the records to back it. For merchandising and planning teams alike.
Every merchandiser and planner works with the full catalog map behind them, so cross-category questions stop being a project and become a query.
Representative scenarios of how retail teams apply a knowledge graph over their commerce data. Illustrative of the pattern, not published client references.
A retailer connects its catalog, supplier, and inventory data in a knowledge graph, resolving duplicate records into single entities. Merchandisers ask which products, suppliers, and substitutes connect to a category and get the answer directly, each result citing its source, instead of a manual data-pull.
With suppliers modeled as connected data, the team asks what products, categories, and revenue depend on a single supplier, and the exposure comes back as one query, traceable to the underlying records, instead of a spreadsheet reconciliation.
However complex the data landscape underneath, the engagement itself stays simple.
You don't have to boil the ocean. Start with a single business context and prove it there. Once that foundation is laid properly, the same connected data tends to open opportunities in other departments, so the next team builds on the work already done rather than starting from zero.
We ingest the content and data you already hold, straight from the systems you already run. Nothing is replaced. Your teams keep working where they work today.
A scoped 8–12 week pilot structures your first decision context. Your domain experts contribute the knowledge; we do the engineering.
You start asking the questions, and every answer carries a citation back to the source, so you can check it yourself.
A 30-minute conversation about your commerce data landscape: which systems hold your catalog, suppliers, and customer data, and which question a connected view of them would answer first. No deck, no pitch.
The same connected-data approach, applied across sectors. Explore another industry.