Artefact
Editor's pick #1Global data and AI consulting firm accelerating ML adoption for major brands at enterprise scale.
What is Artefact?
Artefact is a global consulting company founded in 2014, headquartered in Paris, with 1,500 employees across 33 offices in 26 countries. The firm partners with 1,000+ clients including Samsung, L'Oréal, Orange, and Sanofi, providing services spanning data strategy, ML model development, AI factory deployments, and cloud AI platforms. Artefact covers end-to-end ML lifecycles for large enterprises seeking industrial-scale AI adoption. (Employee count and client names per Artefact official website.)
Artefact was founded in 2014 and is headquartered in Paris, France. The firm employs 1,500 people and works primarily with clients in retail, healthcare, fintech, media, telecommunications, FMCG sectors. Its primary differentiator is: Enterprise ML at 1,500-consultant scale across 26 countries — strategy, deployment, and AI factory in one firm.
Artefact tech stack and services
| Service area |
|---|
| ML Consulting |
| Data Engineering |
| Custom ML Build |
| Generative AI |
| NLP / LLM |
| Predictive Analytics |
Artefact use cases
Short answer: Artefact is best suited for large enterprises, industrial-scale ML and data strategy.
| Use case |
|---|
| Enterprise AI strategy and ML roadmap |
| AI factory deployment for CPG brand |
| Personalisation engine for retail group |
| LLM-powered product search for e-commerce |
| Data quality and governance programme |
Artefact pricing
Short answer: Artefact uses a t&m, retainer pricing approach. Minimum engagement starts at $50K+.
| Engagement model | Typical range | Best for |
|---|---|---|
| T&M | Variable; depends on team size | Large programmes or team augmentation |
| Retainer | Monthly rate; not public | Ongoing AI engineering |
| Dedicated team | Variable; depends on team size | Large programmes or team augmentation |
Artefact pros and cons
| Advantages | Things to consider |
|---|---|
| +Global delivery footprint: 33 offices in 26 countries | -Minimum engagement well above startup budgets — best suited to large programmes |
| +Named clients include Samsung, L'Oréal, Orange, and Sanofi | -Less suited to short fixed-price ML projects or prototypes |
| +End-to-end: from data strategy to production AI factory | |
| +Strong on cloud AI platforms: Vertex AI, Azure ML, AWS SageMaker | |
| +Industry-specific ML expertise across retail, healthcare, and FMCG |
Artefact vs alternatives
How Artefact compares to the other top Machine Learning agencies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Mid-market teams, full-lifecycle ML ownership. | Full-lifecycle ML ownership — model design, training infrastructure, and drift monitoring in one team | 4.8 | Full comparison |
| InData Labs | Fintech, healthcare, SaaS — production ML with GenAI/RAG. | Deep ML and GenAI specialist with 10+ years of production deployments across regulated industries | 4.6 | Full comparison |
| N-iX | Enterprise teams, ML plus cloud engineering, European delivery. | 2,400+ engineers covering ML, cloud, and data under one firm — strong for large multi-track programmes | 4.4 | Full comparison |
| Sigmoid | Fortune 500 retail/CPG/financial firms, AI-first data platforms. | Sequoia-backed AI and data engineering specialist with a Fortune 500 client portfolio in retail and CPG | 4.3 | Full comparison |
| Scopic | Healthcare, fintech enterprises — genuinely custom ML. | 20-year track record of custom ML engineering across 1,000+ projects — no API-wrapper shortcuts | 4.2 | Full comparison |
| Miquido | Product companies, ML embedded in mobile/web products. | AI-plus-product development — ML capabilities integrated with UX engineering, not delivered as a standalone model | 4.2 | Full comparison |
| NineTwoThree AI Studio | Mid-market scale-ups, boutique AI/ML studio. | Inc. 5000 AI studio with Clutch Top 50 ranking — boutique delivery model with direct principal access | 4.1 | Full comparison |
| RTS Labs | US finance/healthcare firms, pilot-to-production AI. | Pilot-to-production ML with deep data engineering roots — Snowflake, Azure, and AWS native | 4.1 | Full comparison |
| SciForce | Production NLP/CV systems, cost-effective Eastern Europe. | End-to-end ML delivery — from requirements to post-launch support — with NLP and computer vision depth | 4.0 | Full comparison |
| LeewayHertz | Enterprise clients, AI engineering, Hackett-backed. | Backed by The Hackett Group since Sept 2024 — AI engineering within an enterprise transformation consulting firm | 4.0 | Full comparison |
| DATAFOREST | US/EU companies, affordable AI, verified Clutch ratings. | 4.9-star Clutch rating across 27 verified reviews — one of the highest-rated AI firms in Eastern Europe | 4.0 | Full comparison |
| Kanerika | US enterprises, AI strategy plus data engineering. | Enterprise data-to-value specialist — ML consulting plus data integration and process automation in one engagement | 4.0 | Full comparison |
| DataArt | Enterprises, established firm, fintech/travel ML depth. | 1997-founded, 5,700-engineer global firm — enterprise scale and continuity across ML and software in fintech and travel | 3.9 | Full comparison |
| ELEKS | Enterprise clients, ML within full-service consulting. | 30+ years of enterprise software delivery — ML within a stable, large-org structure for risk-averse buyers | 3.9 | Full comparison |
| Yalantis | Healthcare, fintech — compliance-aware ML plus IoT. | Compliance-first ML delivery — particularly strong for healthcare and regulated fintech with IoT integration needs | 3.9 | Full comparison |
| Avenga | European enterprises, large-scale ML transformation. | Formed from a 2019 merger — 3,800+ engineers across Europe for large ML and digital transformation programmes | 3.9 | Full comparison |
| Intellectsoft | Fortune 500, AI modernisation of legacy systems. | AI modernisation specialist for Fortune 500 mission-critical systems — legacy transformation, not greenfield | 3.8 | Full comparison |
| Azumo | US companies, cost-effective Latin American nearshore ML. | Latin American nearshore delivery — US time-zone alignment with rates below fully on-shore alternatives | 3.8 | Full comparison |
| Iflexion | Enterprises, ML within software modernisation. | 25 years of software delivery with ML integrated — 800+ clients provide a verified delivery track record | 3.8 | Full comparison |
| Altamira | Companies, production-ready AI agents from day one. | AI-native product-build firm — delivers fully integrated, trained AI agents ready for production from day one | 3.8 | Full comparison |
| Maruti Techlabs | Mid-market companies, cost-effective US/India ML delivery. | Dual US-India delivery with AWS Marketplace listing — cost-effective ML for mid-market budgets | 3.8 | Full comparison |
| Keyrus | International enterprises, industrial-AI implementation. | From experimental AI to industrial AI — consulting group specialising in productionising ML for large organisations | 3.8 | Full comparison |
| Itransition | Global enterprises, ML within full software delivery. | 25+ years of full-cycle delivery to 30+ countries — ML within a large proven software engineering organisation | 3.8 | Full comparison |
| Turing | Companies needing rapid, vetted ML staff augmentation. | AI-vetted 4M+ developer network — fastest route to pre-screened ML talent for staff augmentation | 3.8 | Full comparison |
| Acropolium | SaaS startups, ML features in custom product builds. | 22 years of bespoke product engineering — ML as a product feature, not a standalone model delivery | 3.8 | Full comparison |
| Kanda Software | Healthcare, pharma — compliance-aware AI development. | Regulatory-domain ML specialist — AI for pharma and healthcare with compliance and IP ownership built in | 3.7 | Full comparison |
| Binariks | Companies, cost-effective ML with cloud/IoT integration. | Multi-cloud and IoT-integrated ML delivery — AWS, GCP, and Azure with IoT sensor data pipelines | 3.7 | Full comparison |
| Centric Consulting | US enterprises, ML within business transformation. | Business-outcome ML consulting — AI within management transformation, not pure technology delivery | 3.7 | Full comparison |
| Space-O Technologies | Startups/SMBs, accessible ML in healthcare/e-commerce. | Budget-accessible ML for startups — low minimum engagement with India-based rate advantage | 3.7 | Full comparison |
| Modak | Large enterprises, AI-driven data modernisation. | ML-powered data engineering — uses ML itself to accelerate data prep and modernisation at enterprise scale | 3.7 | Full comparison |
Artefact FAQ
What is Artefact?
Artefact is a global consulting company founded in 2014, headquartered in Paris, with 1,500 employees across 33 offices in 26 countries. The firm partners with 1,000+ clients including Samsung, L'Oréal, Orange, and Sanofi, providing services spanning data strategy, ML model development, AI factory deployments, and cloud AI platforms. Artefact covers end-to-end ML lifecycles for large enterprises seeking industrial-scale AI adoption. (Employee count and client names per Artefact official website.)
How much does Artefact charge?
Artefact uses t&m, retainer pricing. Minimum engagement starts at $50K+. A discovery call is required to get project-specific quotes.
What tech stack does Artefact use?
Artefact works with Python, Vertex AI, Azure ML, AWS SageMaker, Databricks, Snowflake, LLMs, TensorFlow, PyTorch. Primary industries served include retail, healthcare, fintech, media, telecommunications, FMCG.
Is Artefact right for enterprise?
Large enterprises, industrial-scale ML and data strategy. 1,500 team size. Key consideration: Minimum engagement well above startup budgets — best suited to large programmes.
What are the best Artefact alternatives?
The best alternatives to Artefact depend on your use case. Top options are:
- Tensorway: full-lifecycle ml ownership — model design, training infrastructure, and drift monitoring in one team
- InData Labs: deep ml and genai specialist with 10+ years of production deployments across regulated industries
- N-iX: 2,400+ engineers covering ml, cloud, and data under one firm — strong for large multi-track programmes