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#3 of 9 AI agencies in Amsterdam

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Xomnia

Amsterdam, Netherlands

50-249Contact for ratesFounded 2013

About

Xomnia is an Amsterdam data and AI consultancy building machine-learning systems and the data platforms underneath them for Dutch enterprises and public organizations.

Who They Work With

Dutch enterprises whose model problem turns out to be a data-platform problem.

Based on 3 client engagements with a source URL · researched 4 Aug 2026

Our Take

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
Gabor Kiss

Reviewed by Gabor Kiss

Founder & Curator, AIAgencies.eu · 4 Aug 2026

Based on the agency's publicly available portfolio. Own this agency? Claim your profile to provide additional context or request a review update.


How we evaluate agencies

Notable Clients

Every engagement documented in a published case study

Documented engagements: Enexis, VodafoneZiggo, and The Ocean Cleanup. All 3 carry a source URL on file.

EnexisPredictive risk model for dangerous excavation activity near grid infrastructure; logistic regression with MLflow and Airflow in productionSource
VodafoneZiggoCustomer-facing RAG search in production, reported as raising satisfaction and cutting support workloadSource
The Ocean CleanupScalable Azure data ingestion system underpinning the organization's analysisSource

Industries

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