01
Architecture
Guidance on where vector search should live: inside an existing database, as a dedicated vector store, or in a hybrid architecture.
AI
Vector databases and embedding models for retrieval-focused AI systems.
a.s.k. solutions helps teams design and implement practical AI foundations for semantic search, retrieval pipelines, and modern data-driven applications.
Where the focus is
The emphasis is on useful AI infrastructure rather than vague claims: vector storage, embedding strategy, retrieval quality, operational fit, and integration with existing systems.
Typical use cases
- Semantic search across documents and internal knowledge
- Retrieval pipelines for question-answering and assistant workflows
- Hybrid search combining metadata, keyword, and vector approaches
- Practical AI enrichment for databases and applications
Vector databases
Choosing and structuring the vector layer
The right setup depends on the data, latency needs, scale, retrieval quality targets, and operational constraints.
02
Indexing strategy
Chunking, metadata design, filtering, similarity choices, and ingestion structure that improve retrieval quality in practice.
03
Operations
Backup thinking, refresh workflows, monitoring, and lifecycle planning so the AI layer remains maintainable over time.
Embedding models
Selecting models for the actual retrieval problem
What matters
- Language coverage and domain fit
- Embedding quality versus operational cost
- Model size, latency, and hosting constraints
- Consistency across indexing and query-time usage
- Evaluation against real search or retrieval scenarios
How we approach it
Model selection should be tied to measurable outcomes. The goal is not to chase fashionable model names, but to choose an embedding setup that gives strong retrieval quality and fits the environment operationally.
Delivery areas
How this work is typically delivered
Advisory and architecture
Help defining the right retrieval architecture, storage approach, and model strategy before implementation starts.
Prototype to production
Support moving from an initial proof of concept to something that can be operated, measured, and improved.
Database-aware AI integration
Strong database experience helps connect AI retrieval workflows with the wider platform and data landscape.
Next step
Need help with vectors, embeddings, or retrieval design?
Share the data type, retrieval goal, and current platform. A short technical summary is enough to start.