AI blog

What if an AI could evaluate the quality of another AI? That’s exactly what the LLM-as-a-Judge method allows. Increasingly adopted, it offers a fast, flexible, and reliable way to assess the relevance, clarity, or accuracy of responses generated by language models. In this article, we’ll explain why it works, how to implement it, and why it’s now a core part of our approach at Strat37.

In a world saturated with data, businesses are still struggling to turn dashboards into effective decisions. Despite advanced visualization tools, information overload, lack of clear summaries, and lack of business interpretation hold back action. What decision-makers are really looking for are answers that are simple, contextualized and directly usable.

A simple but critical inconsistency compromises the quality of your AI analyses: using English prompts on French documents. This error leads to terminological distortions, dangerous approximations, and unwanted reformulations. The solution: align the language of the prompt with that of the source document for 40% more accurate analyses.

AI is revolutionizing document management by automating the classification, indexing and extraction of key information in all types of documents (PDF, contracts, invoices, etc.). Thanks to intelligent search and document chatbots, teams save valuable time, reduce errors, and ensure regulatory compliance (GDPR). Result: document management becomes a lever for efficiency, traceability and innovation for industry and services.

DataOps transforms business data management by structuring internal processes to ensure reliable, accessible and usable information in real time. Unlike the traditional approach where data projects often fail due to lack of methodology, this DevOps-inspired approach combines transversal collaboration, intelligent automation and rigorous quality control, allowing organizations to transform their raw data into a strategic competitive advantage while democratizing their access to all collaborators. RetryClaude can make mistakes. Be sure to check his answers.

A data project risks failure when deadlines are inexplicably longer, when teams are exhausted looking for data rather than analyzing it, when multiple versions circulate without control, when business-technical communication breaks down and when the first results disappoint. To redress the situation, establish clear planning, automate collection processes, centralize data in a single repository, promote interdisciplinary dialogue, and define specific KPIs from the start. RetryClaude can make mistakes. Be sure to check his answers.

The multiplicity of data formats (Excel, PDF, ERP) generates errors, information losses and limited analyses. To remedy this, automation is essential: OCR extraction, transformation scripts, a single repository, and standardized formats. The benefits are immediate: time savings (70%), reliable decisions and technological agility, turning this challenge into a competitive advantage. Trying again Claude can make mistakes. Be sure to check his answers.

The quality of the product repository is often the determining factor in the success of e-commerce projects, well beyond technology. However, aligning IT, business, and sales teams around these product attributes remains a major challenge. This article explores the challenges of this alignment and proposes concrete strategies to transform your framework into a real performance driver.

In an increasingly digitized economic environment, data management has become not only a technical and operational issue, but also a legal one. Regulations like the GDPR impose strict requirements on all businesses, while poor data quality is an often underestimated risk. This article explores compliance issues related to data and proposes concrete strategies to transform this regulatory constraint into business opportunities.

Data compartmentalization in business is one of the major obstacles to innovation and operational performance. These “data silos” limit collaboration, slow down decisions, and prevent the valorization of information capital. Learn how to effectively break down data silos to unleash your organization's potential and create true sustainable competitive advantage.

The migration or merging of databases is a critical moment in the digital transformation of businesses. Without careful attention to data quality, these projects can quickly turn into operational and financial nightmares. This practical guide introduces you to the challenges, mistakes to avoid and best practices to make your migrations a lasting success.

In the modern retail world, the quality of product sheets directly determines business performance. However, their management remains mostly manual, mobilizing up to 30% of the time of product teams. Artificial intelligence is now transforming this constraint into a competitive advantage, while freeing your teams from repetitive tasks to focus on strategy and innovation.

In an economic environment where responsiveness makes the difference, SMEs and ETIs that effectively structure their data gain a decisive competitive advantage. However, the majority of French companies continue to manage their information assets in a dispersed and obsolete manner. This article explains how to transform this data chaos into a real driver of growth, with concrete steps and measurable benefits for your organization.

Data is now considered the new black gold for businesses, but for many organizations, it remains trapped in crippling digital disorder. This data chaos generates invisible but substantial costs: time wasted searching for information, risky decisions based on erroneous data, and compromised agility. Discover how to identify this problem and, most importantly, how to transform this disorder into a true growth lever through a structured approach to your information assets.RéessayerClaude peut faire des erreurs. Assurez-vous de vérifier ses réponses.

AI diagnostic is a methodical 4-step approach allowing companies to identify and prioritize opportunities for applying artificial intelligence in their specific context. Through a structured process of 10 days of guidance (framing, assessment, use case identification, recommendations), organizations obtain a clear vision of the most relevant AI projects to develop and a concrete action plan for their implementation. This pragmatic approach transforms AI from an abstract concept into projects with measurable added value, while raising awareness among teams about the challenges of digital transformation.