# Adastra vs Artefact

> Comparing AI Consulting options: Frankfurt vs Paris. 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/adastra-vs-artefact
Generated: 2026-09-17 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | Adastra | Artefact |
|---|---|---|
| Editorial score | 74.8/100 | 74.8/100 |
| Hourly rate | not published | not published |
| Team size | 250+ | 1000+ |
| Location | Frankfurt, Germany | Paris, France |
| Founded | 2000 | — |
| Last reviewed | 2026-09-16 | 2026-08-05 |
| Services | AI Consulting, AI Development, Generative AI | AI Consulting, AI Development, AI Automation, AI Agents |
| Industries | FinTech, Manufacturing, Public Sector | E-commerce, FinTech, Manufacturing |

## Our take on Adastra
Reviewed 2026-09-16 by Gabor Kiss against published evidence.

NLB is the study that settles the deployment question: the Slovenian bank put an agentic-AI control layer and its first use case into production in five months, with identity-based approvals through Entra ID, separated development, test and production environments, cost monitoring on Grafana dashboards and a published 80% cut in the time and cost of standing up each further use case.

Two more named systems sit behind it—GenAI enterprise search for KWS on Amazon Bedrock, OpenSearch and SageMaker, delivered inside a Microsoft Teams channel, and a print-bundling optimizer for GZ Media that went from proof of concept to a production application with errors reported down to zero.

Depth across the rest of the library is uneven: much of it is Power BI, Microsoft Fabric and Databricks reporting work rather than AI, the Magna Bohemia planning story is written in the future tense with no measured outcome, and the GZ Media headline states a six-month ROI that its own body describes as the proof of concept's projection.

The weaker fit is buyers who want a small senior team on one model end to end—the pattern here is platform and governance work staffed from a practice the site puts at more than 150 AI specialists across six countries.

### Key strengths
- Client executives go on the record by name and title—NLB's CIO and AI architect, GZ Media's print production director, Magna Bohemia's CEO—so references are traceable before the first call
- German delivery is not a sales address: four offices with street addresses in Frankfurt, Wolfsburg, Munich and Hannover, a named German CEO and a named German AI lead
- The German-language index lists further AI engagements at E.ON, KUKA, ams OSRAM and Hyundai, so the German book is not one story deep

### Good to know
- No rate is published anywhere read—ask for a blended day rate and for the split between German and Czech or Slovak delivery before comparing bids
- The 150-plus AI specialists and 20-plus years are the firm's own figures—ask how many sit in the German practice and who would staff your engagement
- Governance is the visible strength; if you need the model built and evaluated, ask for the evaluation method and accuracy numbers behind a delivered use case

## 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

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

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 and AI Agents

## Questions buyers ask

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

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