FiftyFive built an embeddable vector database combining a high-performance C++ HNSW index with persistent LSM storage, BM25 keyword search, and metadata filtering. The engagement covered AI/ML engineering, C++ and Python development, transactional recovery, and performance optimisation across the complete retrieval stack.
Build Your Platform With UsThe client needed a high-performance vector database combining semantic search, persistent storage, keyword retrieval, metadata filtering, and reliable recovery inside an application-owned environment rather than an externally hosted service.
The engine had to keep an in-memory HNSW graph, persisted document store, and transaction log consistent through inserts, deletes, rollbacks, crashes, and restarts while maintaining retrieval speed and relevance.
FiftyFive built a single embeddable engine layering a C++ HNSW index over persistent LSM storage. A hybrid query layer combines semantic and BM25 retrieval, while WAL recovery and deterministic ID mapping keep vector and document state aligned.
FiftyFive built durable LSM-based storage beneath the vector index for predictable writes and transactional persistence.
FiftyFive implemented graph-based approximate nearest-neighbour search in C++ for high-dimensional vectors.
FiftyFive combined HNSW semantic similarity with BM25 lexical search and pre-scoring metadata filters.
FiftyFive added configurable optimisation for relevance quality, recall, and memory use.
FiftyFive built operational safeguards for embedding the database directly into production applications.
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FiftyFive delivered one embeddable architecture for persistent vector search and hybrid information retrieval, providing semantic search, keyword retrieval, durable storage, and recovery without an external hosted dependency.
Vector search runs inside the application environment without a separately hosted database service.
Combined HNSW similarity and BM25 matching improve conceptual and exact-term relevance.
Transactional LSM storage and WAL recovery keep vector and document states consistent.
Tunable indexes, compression, and optional IVF balance recall, latency, and memory.
A vector database stores numerical embeddings and retrieves results by similarity, using approximate nearest-neighbour indexes such as HNSW for semantic search, recommendations, and RAG.
HNSW is a layered graph index that finds nearby high-dimensional vectors efficiently, with configurable construction and search parameters balancing recall, build time, and latency.
Embeddable databases remove network hops and external dependencies while keeping data inside the application environment. Hosted databases reduce operational setup but add service dependency and latency.
Hybrid search combines semantic vector similarity with keyword retrieval such as BM25, improving relevance for both concepts and exact identifiers, codes, or rare terms.
Filter pushdown narrows candidates before expensive vector scoring, avoiding computation on results that would later be removed by exact, range, OR, NOT, or multi-key conditions.
Production RAG requires persistent document storage, vector indexing, keyword search, metadata filters, ranking, consistency guarantees, and crash recovery in addition to embeddings and an LLM.
HNSW deletions can use ghost-node marking to preserve graph connectivity, exclude deleted records from results, and later rebuild the index to reclaim structure safely.
Product Quantization compresses embeddings into smaller representations, allowing larger indexes within the same memory budget with a controlled trade-off in retrieval precision.
Write-ahead logging records changes before application, while corruption detection and tiered restoration recover committed state and keep the index aligned with document storage.
Custom vector databases support AI, analytics, LegalTech, healthcare, financial services, and enterprise knowledge applications with proprietary data, residency, or latency requirements.
Cost and timeline depend on index types, persistence guarantees, hybrid retrieval, filtering, compression, recovery behaviour, operational controls, and performance requirements.
Vector database work needs systems engineers experienced in C++, index structures, persistence, and performance, alongside AI engineers who understand embeddings, ranking, retrieval, and evaluation.
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