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Data quality: Editorially Reviewed
Google Cloud
Language (LLM / NLP)
Financial services
Databricks
Data engineering & preparation
AI strategy & roadmap consulting
Managed AI operations
Customer & revenue
Implementation & integration
Predictive / classical ML
Personalization
Consumer goods
Healthcare & life sciences
ML lifecycle & data
Change management & adoption
Manufacturing
Snowflake
Amazon Web Services
AI strategy & leadership
Azure
Recommender systems
Data strategy & engineering
Retail & e-commerce
Agentic / autonomous
Enterprise
Learning & Talent
Training & learning
Customer support automation

Artefact

AI strategy & roadmap consulting

Offered by Artefact

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Overview

Artefact's offer rests on AI Strategy and Value Acceleration, Solutions Engineering (data platforms, production AI, agentic workflows, MLOps and LLMOps) and AI People Transformation including the AI Academy. Further lines include data governance, customer data platforms, marketing ROI and AI for customer care. It lists partners across cloud (AWS, Azure, Google Cloud, Alibaba Cloud, Oracle, Snowflake), model providers (Anthropic, OpenAI, Gemini, Mistral) and data platforms (Databricks, Dataiku, Palantir). Sectors include FMCG, retail, luxury and beauty, financial services, healthcare, manufacturing, telecom and the public sector. Its Paris headquarters is on rue Richer, with offices in 25+ countries.

Pricing model
On request
Regions served
Global, Europe, North America, Asia-Pacific (APAC)
Categories
Google Cloud, Language (LLM / NLP), Financial services, Databricks, Data engineering & preparation, AI strategy & roadmap consulting, Managed AI operations, Customer & revenue, Implementation & integration, Predictive / classical ML, Personalization, Consumer goods, Healthcare & life sciences, ML lifecycle & data, Change management & adoption, Manufacturing, Snowflake, Amazon Web Services, AI strategy & leadership, Azure, Recommender systems, Data strategy & engineering, Retail & e-commerce, Agentic / autonomous, Enterprise, Learning & Talent, Training & learning, Customer support automation
Last updated
7 October 2026
Published
7 October 2026

Use cases

Customer & revenue
Personalization
ML lifecycle & data
Training & learning
Customer support automation

Data sources & provenance

Where this information came from, how it was checked and when it was last looked at. “Last updated” records a change to the record — it does not mean the record was verified.

Data quality
Data quality: Editorially Reviewed
Primary source
artefact.com
Sources cited
2
Last reviewed
6 October 2026
Last updated
7 October 2026

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