# Artefact vs Faculty

> Comparing AI Consulting options: Paris vs London. 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-faculty-ai
Generated: 2026-09-20 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | Artefact | Faculty |
|---|---|---|
| Editorial score | 74.8/100 | 73.3/100 |
| Hourly rate | not published | not published |
| Team size | 1000+ | — |
| Location | Paris, France | London, United Kingdom |
| Founded | — | 2014 |
| Last reviewed | 2026-08-05 | 2026-08-04 |
| Services | AI Consulting, AI Development, AI Automation, AI Agents | AI Consulting, AI Development, AI Automation, AI Agents, Generative AI |
| Industries | E-commerce, FinTech, Manufacturing | Public Sector, Healthcare, Legal, FinTech |

## 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 Faculty
Reviewed 2026-08-04 by Gabor Kiss against published evidence.

Published case studies show systems that survived scrutiny most agencies never face—the NHS AI Lab's model-validation process for clinical AI, deployed generative-AI tooling inside Tide's support operation, and an LLM-backed recommender that now sources 25% of Axiom Law's hires.

The published work index spans defense, energy, insurance, and government, though many entries are anonymized or brief.

Rates are not published and the engagement profile is institutional—expect procurement-grade process rather than a lightweight pilot.

The weaker fit is a small team wanting a fast, inexpensive proof of concept; this bench is built for work where failure has consequences.

### Key strengths
- Published 10% ticket-handling-time reduction at Tide, with the deployed tools named (AgentAssist, MemberSummarise on Amazon Bedrock)
- Applied AI since 2014 with an in-house fellowship talent pipeline—delivery here predates the LLM wave
- NHS England's deputy director of AI is quoted crediting the validation work in the published study

### Good to know
- Much of the work index is anonymized (military, challenger bank)—ask for a speakable reference in your sector
- No published rates; budget for institutional procurement timelines

## Which to choose
Choose Artefact for:
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in E-commerce / Manufacturing

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

## Questions buyers ask

### Which has more FinTech experience, Artefact or Faculty?
Both agencies show documented FinTech work. Faculty has the broader industry stack overall, with disclosed experience in Public Sector, Healthcare, Legal beyond their shared focus.

### Which scores higher overall, Artefact or Faculty?
Artefact scores 74.8/100, 1.5 points higher than Faculty at 73.3/100. Both portfolio quality and business credibility feed into the score — see our methodology for the full breakdown.

### When should I consider both Artefact and Faculty?
Consider running parallel discovery briefs with Artefact and Faculty 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