# Artefact vs ML6

> Comparing AI Consulting options: Paris vs Ghent. Side by side: published rates, team size, service and industry coverage, and the curator's assessment of both — pairings are never sold.

Canonical page: https://www.aiagencies.eu/compare/artefact-vs-ml6
Generated: 2026-09-20 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | Artefact | ML6 |
|---|---|---|
| Editorial score | 74.8/100 | 74.8/100 |
| Hourly rate | not published | not published |
| Team size | 1000+ | 50-249 |
| Location | Paris, France | Ghent, Belgium |
| Founded | — | — |
| Last reviewed | 2026-08-05 | 2026-08-04 |
| Services | AI Consulting, AI Development, AI Automation, AI Agents | AI Consulting, AI Development, AI Agents, Generative AI |
| Industries | E-commerce, FinTech, Manufacturing | FinTech, Manufacturing, Public Sector |

## Our take on Artefact
Reviewed 2026-08-05 by Gabor Kiss against published evidence.

The Bpifrance engagement is the one to read first. Artefact took agentic AI from experiment to scale across 500 employees at a public investment bank, and what came out of it was not one assistant but more than 1,500 individual agents built by the business teams themselves. That is a different claim from most agentic case studies, which describe a system rather than an adoption curve.

Carrefour is the sharper commercial example: a conversational agent that produces a complete market study for a store opening in two minutes, work that previously took months, with a 15-point improvement in revenue-prediction accuracy alongside it. VINCI Airports runs across seventy sites, Nexans has a data and AI roadmap built on Databricks, and Burger King, FDJ United and Groupe Barrière each publish their own write-up.

More than a hundred further client pages sit behind those, which is the largest published body of work in this register.

The size cuts both ways. This is a 2,500-person firm across thirty-two offices, so the team you meet is not the team you get by default—ask who staffs the engagement and where they sit.

### Key strengths
- Agentic AI evidenced at adoption scale rather than pilot scale: 500 employees, then more than 1,500 agents emerging from the business itself
- Outcome figures published on the cases that carry them—two minutes for a market study, 15 points of revenue-prediction accuracy, seventy airports
- Over a hundred client pages published, so a buyer can find work in their own sector rather than accepting an analogue

### Good to know
- 2,500 people across thirty-two offices—confirm which office delivers, who is named on the team, and how much is subcontracted
- Many of the published cases carry no numbers at all; the strong ones are strong, but the portfolio is uneven on outcomes

## Our take on ML6
Reviewed 2026-08-04 by Gabor Kiss against published evidence.

The published case library runs to 34 studies, and the ones read in full carry before-and-after numbers rather than adjectives—Scout24's HeyImmo assistant moved user satisfaction from 68% to 75% and first-token latency from 8 seconds to under 5, and Syngenta's lab-inspection vision model doubled analysis speed across half a million wells a year.

Studies name the stack down to the component (OpenAI direct API, Pydantic agents, DataDog on Scout24's own AWS; Vertex AI for Syngenta) and describe how the client was left able to run the system without them.

Work spans regulated and public buyers—the national cyber security center, the EU Council newsroom, Flemish highways—alongside industrial names.

The weaker fit is a buyer wanting a fixed quote up front: no rates, headcount, or founding year appear anywhere public.

### Key strengths
- Case studies publish client-side quotes from named engineering managers, not marketing testimonials
- Published outcomes at the national cyber security center include 3x faster vulnerability handling and 70% faster NIS2 response
- Both major clouds are in evidence—AWS and Google Vertex AI appear in separately documented builds

### Good to know
- Two energy studies run anonymized as "Energy Company"—ask for a speakable reference if you need one in that sector
- No published rates, team size, or founding year; anchor with the European range on our rate index and ask who staffs the work
- Delivery sits across four offices—confirm which one your team would come from

## Which to choose
Choose Artefact for:
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in E-commerce
- broader service offering — also covers AI Automation

Choose ML6 for:
- closer collaboration at a smaller team scale
- disclosed specialization in Public Sector
- broader service offering — also covers Generative AI

## Questions buyers ask

### Which has more FinTech experience, Artefact or ML6?
Both agencies show documented FinTech work and have similar industry breadth. Compare directly on the agency profiles: Artefact and ML6.

### Which scores higher overall, Artefact or ML6?
Both score equally well overall (74.8/100). The deciding factor is specialization — see the editorial quotes and Strengths sections above. Full methodology.

### When should I consider both Artefact and ML6?
Consider running parallel discovery briefs with Artefact and ML6 if your project spans multiple workstreams, you want competitive proposals to compare scope and approach, or you're undecided between the specialization angles each brings (see the Best for cards above). Most engagements ultimately go with one — but the parallel-brief phase is a low-cost way to validate fit.

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Source: AIAgencies.eu — the curated register of European AI agencies. Rate provenance: "agency-confirmed" = disclosed via submission or claimed profile; "directory-listed" = Clutch or two agreeing B2B directories (lone unverified sources are never written). Full method: https://www.aiagencies.eu/methodology · Rate index: https://www.aiagencies.eu/rates