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AI and automation

Vector data bases in SIDECUBE digital projects

Vector databases are useful for semantic search, RAG and AI assistants who need to find information in documents.

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Match

When we use vector databases

The technology is chosen after we understand the objective, the rhythm of the team, the level of administration and how much the project needs to increase.

  • RAGWe link the assistant's responses to the selected documents, with defined sources and usage limits.
  • Knowledge baseWe organize the documents and access rules for the information the team needs to find.
  • Semantic searchWe help the user to find information for the purpose of the question, not just the same expressions.
  • Internal assistantsWe are preparing access to procedures and working information, with appropriate limits for each role.

Uses

What can we build with vector databases?

We only use technology where it helps the project to be faster, easier to manage or safer to expand.

01Indexing documentsAI02Semantic searchAI03Recovery contextAI04Inland searchAI

Deliverables

What goes into implementation

Accurate delivery depends on the project, but we keep every documented, tested and prepared implementation for maintenance.

  • Indexing model
  • Chunking documents
  • Embeddings
  • Semantic query
  • Quality monitoring

Connected services

Vector databases usually occur in these projects

Choose the service that best describes your need, then determine if this technology is the right choice.

AI Automation

AI Automation

AI agents, document processing and automation connected to real processes, with rules, logbooks and human verification.

See serviceCustom software

Software solutions

Applications, dashboards and integrations that gather important processes into a more manageable system.

See service

Recommended pages

Services, technologies and guides related to this technology and its use in projects.

Frequently Asked Questions

Before you choose technology

Uses, integration and costs for vector databases.

01 / Match

When that choice makes sense

What projects do you consider Vector Databases for?

Suitable situations: RAG; Knowledge base; Semantic search; Internal assistants. The choice shall be confirmed after analysis of existing requirements, team and solutions.

What are you proposing for Vector Databases?

Relevant examples are: Document indexation; Semantic search; Context recovery; Internal search AI. We determine what serves your objective and what would add complexity without a clear benefit.

02 / Implementation

Integration and deliverables

Can the solution be integrated into an existing project?

We define approved sources, access to data and permitted actions. We start with a limited flow, with human verification for important results or operations.

What could include implementation?

For Vector Databases, we can define in the offer: Model indexation; Chunking documents; Embeddings; Semantic Query; Quality Monitoring. We confirm the deliverables after verification of compatibility and available access.

03 / Operation

Verification and costs

How do you check that implementation works?

We assess the results on representative examples and difficult cases. We check correctness, permissions, errors and cost; where answers are generated, we also assess loyalty to sources.

What costs should be foreseen in the long term?

We design data preparation, integration, subscriptions or use of APIs and monitoring. The estimate shall be based on a test volume and shall include agreed consumption limits.