SEARCH BY MEANING
Ground AI answers
in enterprise data.
Tarantula AI Vector DB stores source records, metadata and embeddings in one PostgreSQL-based system. Standard SQL filters and vector similarity work together to deliver more precise, governed retrieval.
Tarantula AI Vector DB converts documents, images, sensor readings and logs into vectors to find semantically similar data. Manage PostgreSQL relational data, metadata and vectors in one system for the accuracy, operational convenience and scalability required by enterprise RAG and AI search.
WHY TARANTULA VECTOR DB
Flexible vector search with the reliability of a relational database
Lower operational burden
Use familiar PostgreSQL practices for consistent backup, permissions, monitoring and incident response.
Standard SQL
Combine vector similarity with relational conditions such as customer, time period and permissions in one SQL query.
Data consistency
Update source data, vectors and metadata within the same transaction scope.
Index selection
Select and tune HNSW or IVFFlat according to data volume, accuracy and speed requirements.
Lakehouse expansion
Retain large source datasets and long-term history in Lakehouse, vectorizing only the selected search targets.
On-premises AI
Build internal LLM and RAG environments without moving sensitive enterprise data outside the organization.
FLOW DIAGRAM
How enterprise data becomes
the evidence behind an answer
Embedding requires an AI engine and is delivered with specialist AI partners.
OPERATING LARGE VECTOR DATA
Balance accuracy, speed and cost from the design stage
Table and partition design
Separate tables and partitions by business domain, data type and period to limit index size, update workloads and failure impact.
Data-specific pipelines
Documents, images, sensor readings and logs need different preprocessing. Build extraction, cleansing and embedding pipelines for each data type.
Reranking
Rerank initial vector search results to place evidence most relevant to the question and context at the top.
Indexing strategy
Tune indexes and search parameters against accuracy, response time, build time and memory usage.
Selective embedding
Control costs by choosing data according to practical value and freshness rather than vectorizing everything indiscriminately.
INDUSTRY USE CASES
Beyond document search
find similarities in industry data.
Enterprise knowledge and document search
Search policies, manuals, research and consultation history to provide answers with identifiable sources.
- Permission-based filtering
- Document version and time awareness
- Traceable answer evidence
Odor detection
Compare sensor patterns with vectors from previous odor incidents to find potential causes and relevant responses.
- Multivariate sensor embeddings
- Similar-case retrieval
- Connect field-response knowledge
Diagnostic decision support
Retrieve similar historical examination, imaging and clinical cases to support professional judgment.
- Multimodal data
- Metadata-constrained retrieval
- Evidence from comparable cases
Anomaly detection
Identify unusual events in transaction, equipment and security logs, then connect them with similar failures or threat cases.
- Real-time feature vectors
- Anomaly-based prioritization
- Search response history
MARKET & TECHNOLOGY DIRECTION
Vector search evolves alongside on-premises AI demand.
MARKET
Enterprise on-premises AI
The supplied materials describe a 24% compound annual growth rate for the global vector database market and growing on-premises AI demand in Korea’s public and financial sectors for internal data control and regulatory requirements.
SCALING
Large-scale search expansion
The ecosystem is expanding beyond memory-based HNSW toward DiskANN-related extensions and disk-based search at much larger scales.
HYBRID & HARDWARE
SQL integration and hardware acceleration
Hybrid searches combining standard SQL with vector retrieval, SIMD distance calculations and optimization for different CPU architectures are key development directions.
AI PIPELINE
AI within database workflows
Integration with the pgai ecosystem increasingly connects embedding generation and model calls to database workflows.
AI Vector DB is used together with LLM and RAG technologies.
Relational filters
Combine similarity with access, time and business conditions.
HNSW & IVFFlat
Select index strategies for recall, latency, build time and memory.
Lakehouse extension
Keep source history in the Lakehouse and embed only valuable data.