Use Case
Semantic Search with Endee
Search by meaning, not keywords. Handle synonyms, typos, and multi-lingual queries that exact match misses, at production scale.
Capabilities
Built for meaning-based retrieval
Multi-lingual and Cross-lingual
Use multilingual embeddings such as Cohere multilingual-v3 so a Spanish query finds English documents. One index serves every language in your user base with no extra infrastructure. Cross-lingual recall is handled entirely at the embedding layer.
Filtered ANN Search
Apply metadata filters during the ANN graph traversal, category, price range, date, custom tags. Filters run inside the search rather than on the result set, so you get relevance-ranked results that also satisfy business constraints with zero overhead.
Production-grade Throughput
Handle production search traffic on affordable single-node hardware. Endee delivers the highest QPS of all tested vector databases on the Cohere 10M dataset, without cluster management or costly cloud infrastructure.
High Recall at Scale
Endee maintains greater than 99% recall at one billion vectors using the Vector Graph Engine (VGE), which combines HNSW graph algorithms with hardware-aware memory optimization. Tune the EF parameter at query time to trade latency for recall without re-indexing.
Any Embedding Model
Endee is embedding-model agnostic. Use OpenAI text-embedding-3, Cohere embed-v3, all-MiniLM-L6-v2, BAAI/bge-*, Jina, or any custom encoder. The index works with any fixed-dimension dense vector from 64 to 8,000 dimensions.
Sub-5ms Latency
Achieve sub-5ms p99 latency at scale using the Vector Graph Engine and adaptive quantization. Users see results instantly even under high concurrent load, no batching, no warming, no precomputed result caches to maintain.
Process
How it works
Choose your embedding model
Pick a model that matches your use case: OpenAI for English-only, Cohere multilingual for global apps, or a lightweight on-premise model for low-latency edge deployments. Endee supports any fixed-dimension dense vector.
Index your catalog
Create an Endee index with the right precision level. Use INT8 to fit a billion-item catalog in minimal RAM. Store structured metadata alongside each vector for category, price, region, or any custom attribute.
Search with natural language
Embed the user query and call Endee with any metadata filters in a single API call. Results return in relevance order in milliseconds. Apply optional re-ranking for additional precision on the top-k results.
In Practice
What teams build with semantic search
E-commerce Product Search
Let shoppers find products by describing what they need, not by guessing the right keywords.
Job Board Search
Match candidates to roles by understanding intent, "senior backend role" finds all relevant listings.
Document and Policy Search
Enable employees to find policies, contracts, and guides with natural language questions.
Support Article Search
Surface the most relevant help articles for any user-submitted issue, regardless of exact wording.