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.
Solution approach
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
Related products
Mansel focuses supporting this solution area
FAQ
Questions to clarify at the start
What is the first step?
Technical discovery begins with representative data, user groups, security boundaries, and measurable success criteria.
Is a PoC a production system?
No. A PoC tests critical assumptions in a bounded scope; pilot and production also require operations, security, capacity, and maintenance planning.
Next step
Let's assess your use case and existing systems.
Share your document types, knowledge sources, security boundaries and target outcome. We will identify the most appropriate starting scope together.