What Mansel provides
- Design of vector search and object storage scenarios
- Evaluation of components such as PostgreSQL / PGVector and S3-compatible storage
- On-premise, private cloud, and hybrid architecture approaches
Solution
Data Infrastructure
Vector search, object storage, database, and integration layers.
Problem
AI and document intelligence systems are not just model layers. Without the right storage, indexing, access control, backup, and integration design, solutions struggle in production conditions.
What Mansel provides
Data sources, indexing, storage, and service layers are designed together for modern AI applications. Technologies such as PostgreSQL, PGVector, Qdrant, Weaviate, Milvus, and S3-compatible storage can be evaluated by scenario.
Typical use cases
- Enterprise document indexing
- RAG data layer
- Large file and document storage
- Integration layer for ECM modernization
Architecture and capabilities
- Vector database scenarios
- Object storage
- API services
- Secure network and deployment architecture
Enterprise discussion
Discuss your AI, document intelligence, or data infrastructure needs
Mansel works on practical solution approaches that consider real data, security, and integration constraints from the first phase.