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Retail & E-Commerce

AI Engineering

Autonomous Business Unit

We design and build AI Agents, enterprise LLMs and intelligent ecosystems to accelerate innovation, productivity and new business models for Retail & E-Commerce companies.

Design your AI Agents
Build your Enterprise AI

Retalium Approach

A methodology designed to turn any business need into a working, scalable AI system fully integrated with real processes. Our approach reduces time, risk and cost through three levels of construction.

PROTOTYPE

Fast and Versatile AI Agents

Ideal to validate ideas, operational tasks and new business flows directly with teams.

Features:
  • Setup in a few days
  • Continuous interactions
  • Perfect for workshops, internal tests, and PoCs

HYBRID

Prototype Agents + Proprietary LLM

An intermediate level combining prototype flexibility with a more robust backend.

Caratteristiche:
  • Corporate context and memory
  • More consistent responses
  • Ideal for operations and customer-facing teams

PROFESSIONAL

AI Ecosystem on Google Cloud

All company AI becomes a private, secure, scalable and manageable ecosystem.

Features:
  • Dedicated infrastructure
  • Enterprise-level privacy & security
  • API integrations with ERP, CRM, e-commerce, OMS
  • Advanced performance and automation

AI Resource (agent)

An AI Resource is a combination of AI Agents, AI Tools and AI Knowledge that, when orchestrated, perform multiple tasks. The AI Resource can distribute objectives and tasks across different components to achieve complex results.

AI Agents

Autonomous entities operating in dynamic contexts. They make decisions, adapt and achieve complex goals by combining tools and data. They boost efficiency and innovation within the company.

AI Tools

Specialized tools for specific tasks. They automate repetitive processes with predefined rules, ensuring precision and scalability. They free human resources for higher-value activities.

Ai Knowledge

Special repositories of information linked to documents or databases in Retriever format that feed the RAG (Retrieval-Augmented Generation) knowledge base used by LLMs (Large Language Models).

AI Agents operate autonomously, make decisions, and adapt. AI Tools execute concrete tasks with fixed rules. Together they optimize business processes with flexibility and precision. RAG (Retrieval-Augmented Generation) is an advanced AI technique that combines the capabilities of large language models with information retrieval systems. LLMs (Large Language Models) are AI models trained on massive amounts of text data. This broad knowledge base enables them to understand and generate natural language, performing a wide range of tasks like translation, summarization and question-answering.

Retail Applications

  • sales analysis, forecasting, automatic reordering
  • product content creation, ads, technical sheets
  • price and availability optimization
  • store operations support and team training

E-Commerce Applications

  • automated customer care
  • campaign generation, landing pages, SEO, email marketing
  • catalog and marketplace management
  • CRO support and customer journey optimization

AI Ecosystems

The natural evolution of agents: a central corporate intelligence system that combines:
  • A corporate LLM trained on proprietary data and documents
  • Frontend AI Agents (for teams, departments, and clients)
  • Backend AI Agents (for automations and integrations)

What the AI ecosystem becomes:

  • a corporate “brain” that knows products, processes, policies and documents
  • an operational hub that executes tasks autonomously
  • a decision-support system for management, marketing, e-commerce, HR and retail operations

Technology

Google Cloud & Vertex AI

Our infrastructure is based entirely on Google Cloud, ensuring enterprise-grade security, scalability and integration with corporate tools.

Why Google Cloud

Google Cloud is the ideal foundation for building secure and scalable AI ecosystems. It offers databases, serverless services, compute instances and advanced APIs that allow companies to manage data, models and agents in an enterprise-grade, fully integrated way.
  • Secure, certified infrastructure
  • BigQuery, Cloud SQL, Firestore, AlloyDB
  • Cloud Run, Compute Engine, GPU/TPU
  • GCS, Pub/Sub and retail-native integrations
  • Advanced APIs: Vision, Speech, Document AI
  • Centralized logging, monitoring and governance

Vertex AI: the heart of AI ecosystems

Vertex AI is Google’s platform to build proprietary AI ecosystems: models, agents, RAG systems, custom LLMs and ML pipelines integrated into a single safe, controlled, enterprise-ready solution.
  • Model Garden and proprietary LLM OS
  • Training, fine-tuning and model customization
  • Vector Search and built-in RAG tools
  • Full MLOps pipelines
  • Gemini generative & multimodal models
  • Orchestration of agents and integrated tools
  • AI security, policy compliance and governance

Con oltre due anni di esperienza nella progettazione di ecosistemi AI proprietari, Retalium AI Engineering ha sviluppato soluzioni avanzate basate su modelli come Gemma, creando sistemi intelligenti, scalabili e pienamente integrati nei processi aziendali.

Workspace Retalium

The AI Agents Frontend

The Retalium Workspace is the dedicated interface for companies using AI Resources (Agents). Integrated in the multi-service platform, it simplifies configuration, management and use of enterprise AI through centralized, secure and easily integrable tools.

  • AI Resource Manager

    Manage agents, resources and connectors

  • Custom API
    Integration configuration and technical instructions
  • AI Team
    Direct access to backend agents
  • Data
    View data managed and generated by agents
  • Service
    Logs of hours and activities provided by Retalium

Try One of Our Agents

Analysis and Quotation

A dedicated agent for companies interested in the solution.

The system gathers requirements, analyzes processes, evaluates available data and generates a technical proposal covering:

  • AI architecture
  • Types of agents
  • Automation flows
  • Implementation costs
  • Activation roadmap
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