Your knowledge, embedded. Retrieved. Applied.
VectorBase transforms enterprise documents, databases, and institutional knowledge into intelligent, queryable vector spaces — powering accurate AI responses grounded in your data.
Connect facts. Unlock context.
Our knowledge graph layer maps entities, relationships, and semantic connections across your entire data estate — enabling multi-hop reasoning that pure vector search alone cannot achieve.
Combine structured graph traversal with dense vector retrieval for hybrid search that understands both meaning and structure.
Learn About Hybrid Search
Four pillars of intelligent retrieval
Production-grade RAG architectures tailored to your data complexity, compliance requirements, and scale.
Document RAG
Ingest PDFs, contracts, manuals, and reports. Chunk, embed, and retrieve with citation-backed answers for legal, finance, and compliance teams.
Conversational RAG
Multi-turn dialogue with memory-aware context windows. Maintain conversation state while grounding every response in your knowledge base.
Agentic RAG
Autonomous agents that plan, retrieve, and synthesise across multiple data sources. Tool-use orchestration for complex enterprise workflows.
Multimodal RAG
Index images, diagrams, tables, and audio alongside text. Cross-modal retrieval for technical documentation and visual asset libraries.
Key terms, clearly defined
Navigate the vector AI landscape with confidence.
- Embedding
- A dense numerical representation of text, images, or data that captures semantic meaning in high-dimensional space.
- Vector Database
- A specialised database optimised for storing and querying high-dimensional vectors using approximate nearest neighbour search.
- Chunking
- The process of splitting documents into smaller segments before embedding, balancing context preservation with retrieval precision.
- Re-ranking
- A second-pass scoring step that refines initial retrieval results using cross-encoder models for higher relevance.
- Hybrid Search
- Combining dense vector similarity with sparse keyword matching (BM25) for robust retrieval across diverse query types.
- Grounding
- Anchoring LLM outputs to retrieved source documents, reducing hallucination and enabling verifiable citations.
Built for every scale
From startups to global enterprises and academic institutions.
Enterprise
Dedicated vector clusters, SSO integration, audit logging, and SLA-backed support for regulated industries.
- Private cloud deployment
- Custom embedding models
- PDPA & SOC 2 compliance
- 24/7 dedicated support
SMB
Managed RAG pipelines with predictable pricing. Launch your knowledge assistant in days, not months.
- Managed vector hosting
- Pre-built RAG templates
- API & webhook integrations
- Monthly usage reports
Education
Academic pricing for universities and research labs. Index course materials, papers, and institutional archives.
- Research dataset indexing
- Student query portals
- Citation management
- Grant-funded pricing
Decentralised knowledge architecture
VectorBase implements a data mesh approach — each domain team owns their vector collections while a unified retrieval layer federates queries across the entire organisation.
No more siloed search. One semantic interface to all your institutional knowledge.
Trusted across industries
“VectorBase reduced our contract review time by 70%. The citation-backed answers give our legal team confidence that every response is grounded in actual policy documents.”
“We indexed 12 years of research papers in under a week. Our graduate students now query the entire archive semantically — it's transformed how we do literature reviews.”
“The hybrid search capability is a game-changer. Keyword queries for product SKUs and semantic queries for customer support — all from one unified API.”
Ready to embed your knowledge?
Schedule a discovery call and see VectorBase retrieve, reason, and apply your data in real time.
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