Opus Numeris
Opus Numeris is a French consulting and delivery agency specialized in Data & AI, combining business expertise, IT architecture, data engineering, AI and product data management.
The company operates from strategy to execution: data roadmap, governance, architecture, MDM/PIM, modeling, modern data stack, data science, GenAI, automation, AI applications and production deployment. Its approach emphasizes a key point for operations leaders: no AI without AI-ready data, with upstream work on quality, governance, data models, processes and interoperability.
Opus Numeris claims 25 years of projects, 30+ employees and partners, 20+ enterprise and SME clients, as well as a presence in Paris, Grenoble and Lyon. Its cited references include notably Sanofi, Schneider Electric, Legrand, Lyreco and Truffaut.
The company is led notably by Renaud Cochet, Partner, whose background is presented around management consulting, data advisory and technology. The team also combines profiles in data governance, modeling & architecture, AI / data / digital transformation, product ownership, PLM consulting, manufacturing & supply, data science, software architecture and delivery.
Cited references: Sanofi, Schneider Electric, Legrand, Lyreco, Truffaut, Saint-Gobain, BNP Paribas, Groupe BPCE, Galeries Lafayette, BRGM, Spie, Ipsen, Equans, UGAP, Société Générale.
Technologies and partners cited: SAP, Salesforce, Dassault Systèmes, Veeva, Aras, Infor Nexus, GS1, ISO IDMP, eCl@ss, Snowflake, Databricks, Informatica, Microsoft Azure, Power BI, Collibra, Data Galaxy, Qlik, Kafka, Syndigo, OpenAI, Mistral AI, Gemini, n8n, Make, Perplexity, Dataiku, Anthropic.
Opus Numeris is particularly relevant for industrial, retail, pharma or energy organizations looking to move from AI experimentation to operational industrialization. Its positioning covers data foundations — governance, master data, MDM/PIM, product modeling, quality — then AI use cases, automation and business applications.
For Supply Chain, Manufacturing, Procurement, R&D or Finance leadership, the value is concrete: secure critical data, harmonize product models, accelerate supplier flows, prepare data for AI and reduce friction between legacy systems, data offices and business functions.