End-to-end vector intelligence
From embedding to retrieval to application — VectorBase delivers the full stack for enterprise knowledge AI.
Vector Database Management
Provision, configure, and operate production vector stores with automated scaling, backup, and disaster recovery across AWS, GCP, and Azure.
- HNSW and IVF indexing strategies
- Multi-region replication
- Namespace isolation and RBAC
- Cost optimisation dashboards
Semantic Search
Replace keyword-only search with meaning-aware retrieval. Users find relevant content even when they don't know the exact terminology.
- Hybrid dense + sparse retrieval
- Faceted filtering and metadata search
- Query expansion and spell correction
- Search analytics and relevance tuning
RAG Pipeline Development
Custom retrieval-augmented generation pipelines designed for your data types, latency requirements, and compliance constraints.
- Chunking strategy optimisation
- Prompt engineering and templating
- Evaluation harness with golden datasets
- Continuous improvement loops
Embedding Model Fine-tuning
Domain-specific embedding models trained on your corpus for superior retrieval accuracy in specialised fields like law, medicine, and finance.
- Contrastive fine-tuning on domain pairs
- Model distillation for edge deployment
- A/B benchmarking against base models
- Version control and rollback
Data Governance & Compliance
PDPA-compliant data handling with encryption at rest and in transit, access controls, retention policies, and comprehensive audit logging.
- PII detection and redaction
- Data residency controls (SG region)
- Consent management integration
- SOC 2 Type II alignment
Integration & API Services
RESTful and gRPC APIs, SDKs for Python, Node.js, and Java, plus pre-built connectors for Slack, Teams, Salesforce, and SharePoint.
- Webhook event streaming
- OAuth 2.0 and SAML SSO
- Rate limiting and API key management
- OpenAPI 3.0 documentation
From discovery to deployment
1. Discovery
Assess your data landscape, use cases, and compliance requirements in a structured workshop.
2. Architecture
Design the optimal vector stack — embedding models, index types, retrieval strategies, and LLM integration.
3. Build & Index
Ingest, chunk, embed, and index your data. Configure retrieval pipelines and evaluation benchmarks.
4. Launch & Optimise
Deploy to production with monitoring, iterate on retrieval quality, and scale as your knowledge base grows.
Production-ready retrieval pipelines
See how VectorBase RAG solutions transform raw documents into intelligent, queryable knowledge — with full citation traceability and compliance guardrails.
View VEC-* Solutions