Embedding Fine Tuning Services
Packages & Pricing.

Embedding model fine tuning services for niche vocabulary

Embedding model fine tuning services teach a search model the language of your field, so a query for one internal term finds documents that use its synonyms and abbreviations. Omeiro fine-tunes dense models such as BGE or Voyage on pairs drawn from your own corpus.

Higher tiers generate the training pairs for you, benchmark retrieval and re-index your vector database.

Embedding model fine tuning: preparing data that teaches the model

Training pairs are the product. Embedding model fine tuning learns from examples of a query matched with a passage that answers it, so quality beats quantity. The best pairs come from real user searches, support tickets or documented questions, not from text invented in isolation. Before ordering, collect sample queries, note terms staff use differently from the documents, and gather a list of acronyms and abbreviations. These show where a general model confuses meanings.

Measure first and keep a test set aside. Embedding model fine tuning can overfit to its own pairs, so some questions must remain unseen and be used only for scoring. Because new embeddings change every vector, your database has to be re-indexed afterwards, and that step needs planning around downtime. Tell us the size of your corpus and which vector store you use, so the re-index and any hosting are scoped realistically.