Embedding: meaning becomes computable
The flinq Embedding API turns construction data into vectors. Software can then search and compare by meaning instead of exact wording.
Endpoints: POST /v1/embeddingsPOST /v1/search
What is an embedding?
An embedding is a list of numbers, a vector, that describes the meaning of a piece of content. A model computes such a vector for each item, for example a bill-of-quantities line item or a product datasheet. Content with similar meaning lands close together, regardless of wording.
That makes similarity computable: instead of comparing strings, software compares positions in the vector space. This is the foundation for semantic search, matching, RAG, deduplication and clustering. Once embedded, search is pure vector math and answers in milliseconds.
What does that mean for construction?
Construction data is heterogeneous: bills of quantities, BIM models (IFC), construction-site images, product and cost data. The same work item is worded differently in every document, and word-based search regularly fails on that.
The cross-modal flinq model embeds all these data types into the same vector space. A bill-of-quantities line item, the matching IFC component and the manufacturer product sit on nearby vectors and become findable across format and manufacturer boundaries. On the BauSatz benchmark, flinq reaches nDCG 0.78 on product retrieval versus 0.60 for OpenAI.
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