Components of an AI Infrastructure

The fundamental architecture for robust and efficient AI agents

A high-performance AI infrastructure relies on several key components that together enable your agents to act autonomously, contextually and aligned with your business objectives.

The essential elements that form the basis of our solutions are:

Vector database

For an AI agent to truly replace or augment an employee, it must have its own "Memory": in-depth contextual knowledge of your company, your processes and your market.

• It is a multidimensional representation of information
  • Vector data organization in three-dimensional space
  • Association of each piece of information (token) with a vector that captures its meaning and relationships
  • Ability to identify subtle semantic connections beyond simple keyword matching
• Operational benefits are fundamental
  • Ultra-fast contextual search even in large document collections
  • Nuanced understanding of requests and intentions
  • Ability to identify relevant information even when differently formulated

Data capture and RAG automation

The relevance of an AI agent depends directly on the timeliness and accuracy of the information available to it.

• Continuous data ingestion
  • Automated capture at every information entry and exit point in the organization
  • Real-time synchronization with your existing systems
  • Preservation of context and essential metadata
• Retrieval-Augmented Generation (RAG)
  • Combining access to specific information with LLM's generative capabilities
  • Enrich answers with precise factual data from your internal sources
  • Drastic reduction in hallucinations and inaccuracies

Tools and capabilities

What transforms a language model into a real agent is its ability to act concretely in your digital environment.

• Library of specialized "Tools
  • Sending emails and messages via your official channels
  • Create and manage calendar events
  • Retrieving, creating and modifying documents
  • Updating and tracking tasks in your project management systems
  • Secure, context-sensitive web search
  • Structured data extraction from websites
• Workflow orchestration
  • Execute complex action sequences involving several systems
  • Managing dependencies and conditions between different steps
  • Dynamic adaptation to intermediate results and exceptions

Prompt engineering

As with an assistant, the effectiveness of an AI agent depends fundamentally on the quality of the instructions given to it, which guide its behavior and use of all the above elements.

• The components of an effective prompt
  • Clear and precise definition of objectives
  • Adequate contextualization to situate the task in its environment
  • Specific instructions on methodology and constraints
  • Detailed requirements for the format and structure of results
  • Concrete examples illustrating expectations for different scenarios
• Subsequent optimization is continuous
  • Iterative adjustment based on observed performance
  • Systematic tests to check robustness against special cases
  • Documentation of best practices specific to your context

This modular, extensible architecture forms the technical foundation on which we build AI agents perfectly adapted to your specific needs, capable of evolving with your organization and bringing immediate value to your teams.