Search Blueprint
Ingest → Embed → Index → Retrieve → Re-Rank → Present. Intelligent search from raw data to ranked results.
Six stages from raw data to ranked results
Click any stage for technical depth.
Data Ingestion
Multi-source connectors, format parsing, incremental sync, deduplication.
Raw data flows in from REST APIs, databases, file stores, webhooks, and third-party SaaS platforms. Format-aware parsers normalize structured, semi-structured, and unstructured content into a clean canonical form. Change-detection and incremental sync ensure only new or modified records enter the pipeline, while deduplication guards prevent redundant processing.
Four retrieval paradigms compared
Each strategy has distinct strengths. Hybrid search combines the best of keyword and semantic for production workloads.
Keyword (BM25)
Term-frequency matching with inverse document frequency weighting. The workhorse of traditional search.
Semantic (Vector)
Embedding-based search that matches by meaning, not surface text. Handles paraphrasing and synonyms naturally.
Hybrid
Fuses dense vector search with sparse keyword matching via reciprocal rank fusion for the best of both worlds.
Multi-Modal
Unified search across text, images, and structured data using multi-modal embedding models.
Embedding selection guide
Your embedding model is the foundation of search quality. We benchmark against your domain data to find the optimal choice.
OpenAI · standard tier
Cohere · Embed
BAAI · BGE (large)
Microsoft · E5 (self-host)
Voyage AI · embeddings
Alibaba · GTE
Benchmarks based on the MTEB leaderboard. Actual performance varies by domain — we run A/B evaluations against your data before committing to a model.
Build intelligent search for your data.
Describe your data sources and search requirements. We'll design the embedding, indexing, and retrieval pipeline.