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.
The same idea behind the open product-data standards schema.org and the GS1 Web Vocabulary, both published as RDF, applied to your own product data, so products, offers, and attributes connect across catalogs and search engines. The same connected records answer EU deforestation due diligence when it applies in December, and the Digital Product Passport when it reaches your category. One layer underneath, rather than a separate tool per regulation.
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. Galaxia, the symbolic engine built by our partner Smabbler, computes that layer straight from your own data: no language model in the loop, and every connection traceable to its source. The graph reflects how you operate, not a generic template.
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.
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.
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.
Galaxia computes a first structure over your real data in hours or days, so you can put questions to it before committing to anything. A scoped 8 to 12 week paid pilot then builds your first decision context against success criteria you set. Your domain experts contribute the knowledge, we do the engineering. If it proves value, you expand from there. If it doesn’t, it doesn’t scale.
You query the connected layer in plain language, and answers come in seconds, accurate and traceable, with citations back to the source so you can check them yourself.
Curious what disconnected data may be costing your organization? The free Data Silo Cost Calculator puts a number on it in about two minutes, with no signup to see the result.
Try the Data Silo Cost Calculator →For a single document with a single answer, plain search or an LLM pointed at your PDFs does the job well, and a knowledge graph would be overkill. The difference shows up on the questions one document can’t answer: which suppliers sit behind a product line, how inventory, margin, and demand connect across them, or where a single sub-tier dependency puts several ‘independent’ SKUs at risk at once. Those answers live in the relationships across your catalog, supplier, inventory, and customer systems and the agreements and specifications behind them, not inside any one of them. A knowledge graph connects the structured systems and the documents into one model, follows the chain across them, and returns each answer with a citation back to the record it came from. Something you can act on, and trust.
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.