AI blog

The debate between open-source AI and proprietary models is often presented as a binary choice, when in reality, everything depends on the need. Proprietary models offer performance and simplicity, while open-source models offer flexibility and control. The challenge is not to choose a side, but to adopt the most appropriate solution according to business constraints, costs and the need for data control.

Generative AI is transforming the use of data in business, but it raises security questions. Rather than avoiding it, it is possible to use it with confidence thanks to proven methods: on-premise models for total control, secure APIs guaranteeing the confidentiality of requests, advanced encryption to protect exchanges, and hybrid approaches combining performance and compliance. The challenge lies in the intelligent integration of AI, ensuring data security at each stage.

SaaS solutions have become ubiquitous, but with the rise of AI, their role is shifting. Large Language Models and autonomous agents allow businesses to automate tasks, interact directly with data, and create custom tools without relying on predefined software interfaces. This raises the question: is SaaS becoming a mere infrastructure layer for AI-driven applications? To stay relevant, SaaS providers must specialize in specific industry verticals and integrate AI with consulting to ensure structured, high-quality data. The future of SaaS may be less visible, more modular, and fully AI-powered.

A leading business school organized three webinars to educate its 320 employees on the challenges and opportunities of artificial intelligence. The goal was to harmonize knowledge, demystify AI, and introduce a roadmap for its integration into professional practices. Through an interactive format and case studies tailored to different profiles (faculty, administrative staff, and support teams), this training helped unite teams around a common AI vision.

Strat37 organized a seminar for 60 executives and managers from a leading insurance brokerage group to raise awareness of the challenges and opportunities of artificial intelligence. The goal was to demystify AI, illustrate its real-world applications, and help executives understand how to integrate this technology into their strategies. Through an interactive format and sector-specific use cases, these leaders identified immediate opportunities and outlined a roadmap for AI adoption within their organization.

A major transport company aimed to improve the monitoring of its commercial and operational performance. The goal was to automate margin and revenue calculations while simplifying access to critical data for faster and more efficient decision-making. With an AI-powered solution, we implemented an interactive dashboard enabling real-time logistics tracking, optimizing costs, and maximizing profitability.

Our client wanted to accelerate access to commercial data, enabling teams to make decisions faster without relying on complex reports or specialized analysts. By integrating a generative AI tool, we automated data extraction and interpretation in real-time, allowing instant analysis and strategic recommendations. This approach reduced decision-making time and improved business responsiveness.

Strat37 developed a Retrieval-Augmented Generation (RAG) platform for a leading insurance company, enabling the extraction of precise information from a vast library of market studies. Using advanced OCR technology and AI algorithms, the solution ensures fast searches and continuously updated data. This approach has optimized decision-making and improved access to strategic insights.

A major player in digital infrastructure optimized its operational reporting using AI integrated into ServiceNow. This solution transformed the management of reports from hundreds of daily field interventions, improving data accuracy and reducing administrative workload. By automating the translation and reformulation of field notes, real-time synchronization with ServiceNow, and workflow optimization, the company significantly enhanced its operational efficiency.

An industrial leader partnered with Strat37 to automate the management and SEO optimization of over 600,000 product pages. The goal was to enhance their search engine rankings by automatically generating precise descriptions and optimized alt tags using artificial intelligence. This approach significantly increased organic traffic and improved the online visibility of the product catalog.

With 4,000 sites in France and daily interventions, our client had to manually process each access request, verifying compliance with a regulatory prevention plan. This time-consuming and demanding process required systematic human verification and caused significant delays. By integrating an AI solution, we automated the analysis of prevention plans and the validation of intervention requests, cutting processing time in half and ensuring full compliance.

Our client faced a major operational challenge: managing over 4,000 emails per year related to power outage incidents across multiple sites. Each email required manual reading, classification, and ticket creation in ServiceNow. This process was not only time-consuming but also prone to delays and human error. By implementing an AI-driven automation solution, we enabled real-time incident analysis, classification, and ticket generation, significantly improving efficiency and response times.

During a merger and acquisition, our client needed to integrate product categories from two distinct systems (PIM), each with its own structures and nomenclatures. With over 3,000 categories to harmonize, a manual approach would have been unrealistic and error-prone. Using artificial intelligence, we automated the analysis, matching, and data integration, ensuring a fast and precise transition.

In this article, we explore how artificial intelligence has revolutionized product information management for a retail company dealing with 1.5 million pieces of unstructured data. Thanks to AI, we have automated the enrichment of product sheets, improved data accuracy, optimized e-commerce preparation and reduced manual processing time. This solution not only increased operational efficiency, but also improved product descriptions for marketing purposes.

In this article, we explore how artificial intelligence revolutionized product information management for a distribution company struggling with 1.5 million unstructured data points. By leveraging AI, we automated the enrichment of product sheets, improving data accuracy, enhancing e-commerce readiness, and reducing manual processing times. This solution not only increased operational efficiency but also optimized product descriptions for marketing purposes.