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Digital Product

Artificial Intelligence

AI is not limited to science fiction. Today, it is transforming the way businesses make decisions, automate tasks, and interact with their customers. At Trinary, we design concrete artificial intelligence solutions tailored to your industry. From product recommendations to anomaly detection, our approach is focused on business impact.

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Put artificial intelligence at the service of your growth

We develop ethical, secure, and useful AI solutions.

What is artificial intelligence?

Artificial intelligence (AI) refers to technologies capable of simulating human cognitive functions such as learning, reasoning, perception, and language understanding. In a business context, AI enables automation of analysis, data enrichment, and better decision-making.

Use cases

1

Demand or sales forecasting models

What it is:

These models accurately anticipate future demand for a product or service by leveraging sales history, seasonality, external events, and other variables (price, weather, marketing campaigns, etc.).

Application examples:

  • Help a service company plan human resources (e.g., number of daily interventions).
  • Estimate how many products to produce or order to avoid overstock or stockouts.
  • Predict sales volume during holidays or sales events.

Benefits:

  • Reduction of losses due to poor planning.
  • Better resource allocation.
  • Supply chain optimization.
2

Semantic analysis of documents (natural language processing)

What it is:

Semantic analysis uses AI to understand the meaning and structure of textual documents (contracts, emails, internal notes, forms, etc.). It can extract key information, classify documents automatically, or detect intents and obligations.

Application examples:

  • Analyze customer feedback to identify trends or recurring issues.
  • Extract confidentiality or termination clauses from contracts.
  • Automatically classify incoming emails by topic or priority.

Benefits:

  • Scalable processing of thousands of documents with precision.
  • Reduced risk of misinterpretation or oversight.
  • Time savings for legal or administrative teams.
3

Personalized content recommendation

What it is:

AI analyzes user behavior and preferences to suggest relevant content: articles, products, services, videos, trainings, etc.

Application examples:

  • On a learning platform: offer modules adapted to the learner’s level and pace.
  • On an e-commerce site: recommend complementary or similar products.
  • On an HR intranet: suggest resources based on employee roles or goals.

Benefits:

  • Enhanced user experience and increased loyalty.
  • Better utilization of existing content.
  • Improved conversion rates.
4

Conversational agents (intelligent chatbots)

What it is:

A conversational agent is a virtual assistant capable of answering questions, helping users navigate a site, booking appointments, or even processing transactions—thanks to advanced language models (like GPT).

Application examples:

  • Internal HR assistant to answer employee questions about benefits or company policies.
  • 24/7 customer support answering FAQs, with escalation to a human when needed.
  • Conversational search interface for a company’s knowledge base.

Benefits:

  • Continuous availability, without interruptions.
  • Reduced load on support teams.
  • Improved customer service.

Methodology

  1. Data Audit: evaluate sources, quality, and structure of available data.
  2. Use Case Scoping: validate objectives, feasibility, and success indicators.
  3. AI Modeling: train models (ML, NLP, vision) tailored to your context.
  4. Integration: connect to existing tools via APIs or custom interfaces.
  5. Support: training, documentation, and post-deployment follow-up.

FAQ

No. There are pre-trained models or we can create solutions adapted to small data sets.

Yes. We follow strict security standards and can operate locally or on secure servers to protect sensitive data.

No, AI is not meant to replace your teams—it helps them focus on higher-value tasks.

It depends on complexity. A prototype can be delivered in a few weeks. A full project (from data collection to integration) typically takes 6–16 weeks.

Our clients often see significant time savings, more accurate analysis, and improved data-driven decision-making.

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