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AI and automation

Pinecones in SIDECUBE digital projects

Pinecone is an option for AI projects that need a vector index administered, scalable and connected to applications.

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Match

When we use Pinecone

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.

  • AGR administeredWe establish the update of sources, access and verification of responses in a flow that can be operated constantly.
  • Semantic searchWe help the user to find information for the purpose of the question, not just the same expressions.
  • UI applicationsWe delimitate the tasks of the model, the necessary checks and cases where the result must be reviewed by a person.
  • Knowledge base scalableWe plan to organize and update sources as the volume and number of users increase.

Uses

What can we build with Pinecone

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

01Vector searchAI02Document indexAI03Retrieval for IAAI04RAG PrototypeApplications

Deliverables

What goes into implementation

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

  • Index Configuration
  • Pipeline embeddings
  • API integration
  • Relevant testing
  • Cost monitoring

Connected services

Pinecone usually appears 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 serviceWeb, mobile and SaaS applications

Applications

Web and mobile applications, SaaS platforms and portals connected to company data and processes.

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 Pinecone.

01 / Match

When that choice makes sense

What projects do you consider Pinecone for?

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

What are you proposing for Pinecone?

Relevant examples are: Vector search; Document index; Retrieval for AI; Prototype RAG. 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 Pinecone, we can define in the offer: Configure index; Pipeline embeddings; API integration; Relevant testing; cost 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.