# FELD M

> FELD M is a reviewed AI agency in Munich, Germany, listed in the AIAgencies.eu register and last assessed 2026-08-06.

Canonical page: https://www.aiagencies.eu/agency/feld-m
Generated: 2026-09-17 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
- Location: Munich, Germany
- Website: https://www.feld-m.de
- Hourly rate: not published
- Team size: 50-249
- Founded: 2002
- Services: AI Consulting, AI Development
- Industries: Healthcare, E-commerce
- LinkedIn: https://www.linkedin.com/company/feld-m/

## About
FELD M is a Munich data consultancy covering analytics, data engineering and privacy, with a data-science practice building predictive and classification models.

## Curator assessment
Reviewed 2026-08-06 by Gabor Kiss against published evidence.

Three published engagements are data science, and they are more specific about method than most anonymized work manages.

For a multinational healthcare corporation, document classification was built on deep-learning computer vision—CLIP for one-shot learning and image extraction, an XGBoost classifier as the benchmark—and the write-up compares its own results against AWS Comprehend, beating it on several labels. The pipeline runs in AWS and processes documents in the thousands.

For a media company, a propensity model on Random Forest and XGBoost turns Adobe Clickstream data into subscription predictions, running on Azure and written back into Adobe Analytics for user-level analysis. For an international retailer, a forecasting model in R and SparkR simulates price adjustments across more than 10,000 products, including substitution and cannibalization between them.

All three are anonymized by sector, while the named clients—Der Spiegel, Media Markt, SBB, Hugo Boss, bexio—sit on the analytics, tracking and consent side of the practice. That split is the thing to understand before engaging: this is a data and marketing-technology consultancy with a data-science team inside it, not an AI firm. Best for organizations whose AI ambition is downstream of a measurement problem.

### Key strengths
- Names its models and benchmarks—CLIP, XGBoost, Random Forest, SparkR—and publishes a comparison against AWS Comprehend rather than asserting accuracy
- Twenty years of analytics and marketing-technology work, with named enterprise clients on it
- Privacy and consent is a published specializm, which matters when the model needs first-party data

### Good to know
- Three data-science engagements, all anonymized by sector—ask what the AI team has shipped recently and who staffed it
- The named-client work is analytics rather than AI; do not read the logo wall as AI delivery evidence
- Outcomes are described qualitatively—"highly accurate", "outperformed"—without the figures the technical detail would support

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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