# DEPT vs Xomnia

> Two top-rated AI Development agencies in Amsterdam. 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/dept-vs-xomnia
Generated: 2026-09-21 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | DEPT | Xomnia |
|---|---|---|
| Editorial score | 64/100 | 61.8/100 |
| Hourly rate | €130-170/hr (directory-listed) | not published |
| Team size | 1000-9999 | 50-249 |
| Location | Amsterdam, Netherlands | Amsterdam, Netherlands |
| Founded | 2016 | 2013 |
| Last reviewed | 2026-08-06 | 2026-08-04 |
| Services | AI Development, AI Marketing, Generative AI | AI Consulting, AI Development, Generative AI |
| Industries | SaaS & B2B | Manufacturing |

## Our take on DEPT
Reviewed 2026-08-06 by Gabor Kiss against published evidence.

Signs is the most visible AI work here: a sign-language learning platform using computer vision to check the learner's signing, which reached more than 20 million people in its first week and recorded 20,000 signs learned in ten days. It is a collaboration with NVIDIA and the American Society for Deaf Children rather than a commissioned client project, which is worth knowing when you read it as evidence.

The commissioned AI work is more ordinary and more useful as a guide: a return-value prediction model for the fashion retailer Omoda that fed its marketing hub, Clair for CaryHealth, a reference app its own case study credits with 90% faster clinical search, and nine CGI avatars generated for Inter's INTERISTA fan platform, published as 13.5 times faster and 92% cheaper than the comparable manual workflow.

Size is the real variable: DEPT operates across many markets, so who staffs your project matters more here than at a twenty-person firm.

The weaker fit is an enterprise wanting a production AI system governed and operated long-term; this bench is strongest where AI meets the audience.

### Key strengths
- Named clients with figures attached across three sectors: retail returns, clinical search, sports fan engagement
- Computer vision applied to genuinely hard interaction problems, not generative demos
- International delivery capacity, so multi-market rollouts do not need a second agency

### Good to know
- Signs is collaborative work with NVIDIA, not a client engagement—judge the commissioned cases separately
- Published AI work is weighted toward audience-facing products—ask for an operated system with an SLA if that is what you need
- At this scale, insist on knowing the named team, not the agency's aggregate credentials

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

Xomnia publishes the engineering rather than the pitch: the Enexis excavation-risk model names its stack down to logistic regression, MLflow and Airflow, which tells you the model is versioned and scheduled rather than sitting in a notebook.

VodafoneZiggo's customer-facing search runs on retrieval-augmented generation in production, and The Ocean Cleanup engagement built the Azure ingestion layer underneath the analysis rather than the analysis alone.

Outcomes are reported qualitatively more often than numerically, so the case studies tell you what was built and how, but less about what moved.

Best for Dutch enterprises and public bodies that need the data platform and the model treated as one engagement, because the first is usually why the second has not worked yet.

### Key strengths
- Names MLOps tooling (MLflow, Airflow) in published work, which is evidence of systems built to keep running
- Spans data engineering and modeling, so the platform gap gets addressed rather than escalated
- Clients include a national grid operator and a telecom, both environments with real reliability constraints

### Good to know
- Case studies describe method well and outcomes loosely—ask what was measured after go-live
- No published rates, headcount or founding year

## Which to choose
Choose DEPT for:
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in SaaS & B2B
- broader service offering — also covers AI Marketing
- a higher overall editorial score

Choose Xomnia for:
- closer collaboration at a smaller team scale
- disclosed specialization in Manufacturing
- broader service offering — also covers AI Consulting

## Questions buyers ask

### Which scores higher overall, DEPT or Xomnia?
DEPT scores 64/100, 2.2 points higher than Xomnia at 61.8/100. Both portfolio quality and business credibility feed into the score — see our methodology for the full breakdown.

### When should I consider both DEPT and Xomnia?
Consider running parallel discovery briefs with DEPT and Xomnia 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