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 | Details |
|---|---|
| Enterprise AI strategy and ML roadmap | Available for retail, healthcare, fintech, media, telecommunications, FMCG clients |
| AI factory deployment for CPG brand | Available for retail, healthcare, fintech, media, telecommunications, FMCG clients |
| Personalisation engine for retail group | Available for retail, healthcare, fintech, media, telecommunications, FMCG clients |
| LLM-powered product search for e-commerce | Available for retail, healthcare, fintech, media, telecommunications, FMCG clients |
| Data quality and governance programme | Available for retail, healthcare, fintech, media, telecommunications, FMCG clients |
Artefact use cases
Short answer: Artefact is best suited for large enterprises and major consumer brands seeking industrial-scale ML adoption and data strategy.
| Use case | Industries | Approach |
|---|---|---|
| Enterprise AI strategy and ML roadmap | retail, healthcare | Python, Vertex AI |
| AI factory deployment for CPG brand | retail, healthcare | Python, Vertex AI |
| Personalisation engine for retail group | retail, healthcare | Python, Vertex AI |
| LLM-powered product search for e-commerce | retail, healthcare | Python, Vertex AI |
| Data quality and governance programme | retail, healthcare | Python, Vertex AI |
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 needing custom ML builds with full... | Full-lifecycle ML ownership — model design, training infrastructure, and drift monitoring in one team | 4.8 | Full comparison |
| InData Labs | Fintech, healthcare, and SaaS companies needing production-grade ML... | Deep ML and GenAI specialist with 10+ years of production deployments across regulated industries | 4.6 | Full comparison |
| N-iX | Enterprise teams needing multidisciplinary ML and cloud engineering... | 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, and financial services firms... | Sequoia-backed AI and data engineering specialist with a Fortune 500 client portfolio in retail and CPG | 4.3 | Full comparison |
| Scopic | Healthcare, fintech, and enterprise teams building genuinely custom... | 20-year track record of custom ML engineering across 1,000+ projects — no API-wrapper shortcuts | 4.2 | Full comparison |
| Miquido | Product companies and scale-ups needing ML features embedded... | 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 companies and scale-ups building AI and ML... | Inc. 5000 AI studio with Clutch Top 50 ranking — boutique delivery model with direct principal access | 4.1 | Full comparison |
| RTS Labs | US mid-market companies in financial services and healthcare... | Pilot-to-production ML with deep data engineering roots — Snowflake, Azure, and AWS native | 4.1 | Full comparison |
| SciForce | Companies building production NLP or computer vision systems... | End-to-end ML delivery — from requirements to post-launch support — with NLP and computer vision depth | 4.0 | Full comparison |
| LeewayHertz | Enterprise clients seeking AI product engineering backed by... | Backed by The Hackett Group since Sept 2024 — AI engineering within an enterprise transformation consulting firm | 4.0 | Full comparison |
| DATAFOREST | US and EU companies seeking competitively priced custom... | 4.9-star Clutch rating across 27 verified reviews — one of the highest-rated AI firms in Eastern Europe | 4.0 | Full comparison |
| Kanerika | Mid-to-large US enterprises seeking AI strategy combined with... | Enterprise data-to-value specialist — ML consulting plus data integration and process automation in one engagement | 4.0 | Full comparison |
| DataArt | Enterprises wanting ML services from a large, established... | 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 needing ML within a full-service technology... | 30+ years of enterprise software delivery — ML within a stable, large-org structure for risk-averse buyers | 3.9 | Full comparison |
| Yalantis | Healthcare and fintech companies needing compliance-aware ML consulting... | Compliance-first ML delivery — particularly strong for healthcare and regulated fintech with IoT integration needs | 3.9 | Full comparison |
| Avenga | European enterprise clients seeking large-scale ML and digital... | Formed from a 2019 merger — 3,800+ engineers across Europe for large ML and digital transformation programmes | 3.9 | Full comparison |
| Intellectsoft | Fortune 500 enterprises needing AI modernisation of legacy... | AI modernisation specialist for Fortune 500 mission-critical systems — legacy transformation, not greenfield | 3.8 | Full comparison |
| Azumo | US companies seeking cost-effective nearshore ML development with... | Latin American nearshore delivery — US time-zone alignment with rates below fully on-shore alternatives | 3.8 | Full comparison |
| Iflexion | Mid-to-large enterprises needing AI and ML integrated within... | 25 years of software delivery with ML integrated — 800+ clients provide a verified delivery track record | 3.8 | Full comparison |
| Altamira | Companies needing production-ready AI agents and ML systems... | 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 seeking cost-effective AI/ML consulting with US... | Dual US-India delivery with AWS Marketplace listing — cost-effective ML for mid-market budgets | 3.8 | Full comparison |
| Keyrus | International enterprises seeking a global data and AI... | From experimental AI to industrial AI — consulting group specialising in productionising ML for large organisations | 3.8 | Full comparison |
| Itransition | Enterprises in 30+ countries needing ML consulting integrated... | 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 access to vetted ML engineers... | AI-vetted 4M+ developer network — fastest route to pre-screened ML talent for staff augmentation | 3.8 | Full comparison |
| Acropolium | SaaS companies and mid-market startups needing ML features... | 22 years of bespoke product engineering — ML as a product feature, not a standalone model delivery | 3.8 | Full comparison |
| Kanda Software | Healthcare, pharma, and life sciences companies needing compliance-aware... | Regulatory-domain ML specialist — AI for pharma and healthcare with compliance and IP ownership built in | 3.7 | Full comparison |
| Binariks | Companies seeking cost-effective AI and ML engineering with... | Multi-cloud and IoT-integrated ML delivery — AWS, GCP, and Azure with IoT sensor data pipelines | 3.7 | Full comparison |
| Centric Consulting | US mid-to-large enterprises needing ML consulting integrated within... | Business-outcome ML consulting — AI within management transformation, not pure technology delivery | 3.7 | Full comparison |
| Space-O Technologies | Startups and SMBs seeking accessible, cost-effective ML development... | Budget-accessible ML for startups — low minimum engagement with India-based rate advantage | 3.7 | Full comparison |
| Modak | Large enterprises needing AI-driven data modernisation to prepare... | 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 and major consumer brands seeking industrial-scale ML adoption 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
Compare Artefact with other Machine Learning agencies
Last reviewed: July 2026. Verify all details directly with Artefact before making a decision.