Featrix: Structured Similarity Search for Tabular Data
Featrix is the similarity engine built for real-world data. While traditional vector search tools focus on unstructured text and images, Featrix is purpose-built to handle structured, tabular data—so you can retrieve, compare, and reason over rows of data the way your users actually think.
Whether you're powering retrieval-augmented generation (RAG), anomaly detection, customer support automation, or intelligent data exploration, Featrix makes your structured data searchable by meaning—not just by exact matches or free-text embeddings.
How Featrix Works: Three Steps to Structured Similarity
1. Define Your Table
Select the columns that matter or just go ahead and bring in all 200 of them. Whether it's product info, user attributes, ticket summaries, or mixed types, Featrix lets you specify which fields to include—text, numeric, categorical, or boolean. You stay in control of how your data is interpreted.
2. Generate Smart Rows Embeddings
Featrix encodes each row using modality-aware deep models: text fields get transformer-based embeddings, numbers and categories get learned, type-specific encoders. The result is a single dense vector per row that captures its full semantic meaning.
3. Search by Meaning
Once embedded, you can search for similar rows using vector similarity—powered by Featrix's internal engine or exported to any vector DB like FAISS, Weaviate, or Pinecone. You can also embed new records on the fly for real-time querying in RAG and analytics workflows.
How Featrix Outperforms Alternatives
| Feature | Featrix | Alternatives |
|---|---|---|
| Structured vs. Flattened | Understands each column’s type and meaning — text, number, category, boolean — and encodes them appropriately. | Typical text embedding: Forces entire rows into flat strings (e.g. “CustomerType=SMB | Product=Alpha123”), losing semantic structure and signal. |
| Semantic Relevance vs. Surface Similarity | Finds records that are actually similar — not just phrased similarly. | Text-based search: Can be thrown off by rewording, abbreviations, or column order. Numeric or categorical values often get ignored or misinterpreted. |
| Column Control vs. Black Box | You decide which columns contribute to similarity and how much they matter. | All fields are treated equally (or not at all), with no insight or tunability. |
| Built for RAG & AI Workflows | Drop in as the retrieval layer in RAG pipelines — feeding your LLM structured context that actually matches the query. | Traditional RAG: Designed for document chunks and paragraphs — not tables, transactions, or CRM records. |
| Performance at Scale | Efficient row-level embedding, exportable to vector DBs, built to handle millions of records. | DIY setups: Often slow, error-prone, or limited in handling real-world mixed-type data at scale. |
| Smart Autocomplete | Leverages predictive models trained on your data to intelligently fill in missing values — like suggesting product category from description, or inferring customer tier from behavior — without manual rule-writing. This helps ensure consistent and complete rows before similarity search or downstream tasks. | Typically require manual imputation rules, one-hot hacks, or ignoring rows with missing data altogether. Most vectorization tools simply fail silently or degrade in accuracy when fields are blank or inconsistent. |