AI blog

In every business, data goes a long way before it becomes usable: cleaning, calculations, exploration, scenarios, and then communication. This is the job of analysts, which is essential but often time-consuming, as more than half of the time is spent preparing data rather than learning from it. Artificial intelligence is changing the situation: not by replacing the analyst, but by playing the role of co-pilot, capable of automating repetitive tasks, detecting weak signals and generating scenarios so that humans can focus on understanding and making decisions.

Most businesses only use 20% of their data, which is structured and easy to analyze. The remaining 80% — emails, PDFs, support tickets, customer feedback — are sleeping due to a lack of appropriate tools. AI is changing the game: thanks to embeddings, it becomes possible to link this unstructured data to your business indicators (sales, satisfaction, churn). As a result, your dashboards no longer only show what's going on, but finally reveal why it's happening.

SaaS is too rigid, the service too ephemeral. Between the two, a new model is needed: Service as a Platform. Powered by generative AI, it combines human expertise to understand business challenges and an evolving platform to restore, capitalize and create sustainable value. Discover why this model is already transforming retail, logistics, finance, industry, and real estate.

In industry, a poorly calibrated forecast, a biased indicator, or outdated data can cost millions — in overstocks, in delays, in lost margins. However, these errors do not come from a lack of data... but from information that is incomplete, inconsistent or taken out of context. The role of a specialized AI agency is simple: to transform this mass of raw data into a reliable, enriched and responsive management tool, so that each strategic decision is based on indisputable facts — and not on approximations.

In a world where every interaction, transaction, or operation generates data, businesses have never had so much information at their fingertips. However, as the Bpifrance Le Lab study reveals, a large part of this capital remains unexploited. SMEs and ETIs have files, tables, histories and indicators by the thousands... but this data is dormant, scattered in various systems, incomplete or poorly exploited. The result: strategic decisions taken with a partial vision of reality, even though this information could become a major driver of performance and innovation.

In many businesses, strategic decisions are based on data... not always reliable. Inconsistencies between departments, numbers that arrive too late, anomalies that are not detected: these problems hinder decision-making and are expensive. By combining method and artificial intelligence, it is possible to make your data reliable, enriched and exploited in just 30 days — and to transform the reports received into a real strategic lever.

Every business is sitting on an invisible gold mine: its data. Transactions, customer interactions, market trends... everything is there, but it's often scattered, untapped, or unusable. According to Gartner, 97% of the data collected is never used. And when they are, their poor quality can cost millions every year. However, behind these numbers lies a simple truth: with the right method, raw data can become a powerful competitive advantage. This guide offers a clear path, from initial cleaning to strategic decision, based on key figures and concrete tools.

Do you think that your reports only cost time? In reality, they can snack on up to €10,000 per year and per employee... without anyone noticing. Learn how this hidden cost is undermining your margins and how to eliminate it before it takes hold.

80% of SMEs believe that the quality of their data hampers strategic decision-making. What if your next competitive advantage was simply... better data reliability? This article explores why the quality of your data is a business priority, how AI can transform your relationship with information, and what concrete actions to take today.

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.