# Artefact vs Datatonic

> 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-datatonic
Generated: 2026-09-17 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | Artefact | Datatonic |
|---|---|---|
| Editorial score | 74.8/100 | 74.8/100 |
| Hourly rate | not published | not published |
| Team size | 1000+ | 50-249 |
| Location | Paris, France | London, United Kingdom |
| Founded | — | 2013 |
| Last reviewed | 2026-08-05 | 2026-09-07 |
| Services | AI Consulting, AI Development, AI Automation, AI Agents | AI Consulting, AI Development, Generative AI |
| Industries | E-commerce, FinTech, Manufacturing | FinTech, Healthcare, E-commerce |

## 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 Datatonic
Reviewed 2026-09-07 by Gabor Kiss against published evidence.

The architecture detail is unusually specific for a consultancy site. The AstraZeneca engagement documents a Bedrock RAG pipeline that ingested more than 20,000 scientific documents and 1.5 billion input tokens, with a custom batch-sync engine written to get around a 1,000-document platform ceiling.

Vodafone's AI Booster, built over 18 months on Vertex AI, went live in more than eight markets and cut proof-of-concept-to-production from months to about four weeks; the reusable MLOps templates behind it were released as open source.

The Alpian engagement is a customer-facing banking agent under FINMA supervision, built on Google's Agent Development Kit with a Model Context Protocol server translating questions into SQL so the model never touches the database directly.

Two of the three most recent cases report engineering rather than money, so a buyer who needs a business case should ask what AI Booster and the Alpian agent cost and returned.

### Key strengths
- The AstraZeneca RAG pipeline is documented at 20,000+ documents and 1.5 billion input tokens with the architecture published in full
- Vodafone AI Booster runs in more than eight markets and cut proof-of-concept-to-production by about 80%; its MLOps templates were open-sourced
- Delivery on both Google Cloud and AWS Bedrock, with named Datatonic engineers quoted alongside client-side leads

### Good to know
- The AstraZeneca and Alpian write-ups quantify engineering, not business outcome—ask for the return figure
- Much of the work sits inside Google Cloud partnerships—ask who owns the platform relationship and what it costs after handover
- Seven offices across Europe and Toronto—ask which one staffs your engagement

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

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

## Questions buyers ask

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

### Which scores higher overall, Artefact or Datatonic?
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 Datatonic?
Consider running parallel discovery briefs with Artefact and Datatonic 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