# dida

> dida is a reviewed AI agency in Berlin, Germany, listed in the AIAgencies.eu register and last assessed 2026-08-04.

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

## Facts
- Location: Berlin, Germany
- Website: https://dida.do
- Hourly rate: €130-175/hr (directory-listed)
- Team size: 10-49
- Founded: 2018
- Services: AI Consulting, AI Development, AI Automation
- Industries: Manufacturing, Public Sector, Legal
- LinkedIn: https://www.linkedin.com/company/dida-machine-learning/

## About
dida is a Berlin machine-learning consultancy building custom computer-vision and natural-language systems for process automation. Its published portfolio spans remote sensing, industrial inspection, document understanding and public-sector search.

## Documented clients
Documented engagements: Deutsche Bahn, Deutscher Wetterdienst, Enpal, Carl Zeiss, and Klöckner. 3 of 5 carry a source URL on file.

## Curator assessment
Reviewed 2026-08-04 by Gabor Kiss against published evidence.

The Deutsche Bahn work is the most technically detailed case study on this register. dida published its approach to detecting anomalous objects on track—monocular depth estimation with a fine-tuned vision transformer, Segment Anything for masks, depth maps to rank what matters—along with before-and-after outputs and a conference talk naming the engineer who led it.

The published project index runs to roughly eighteen builds across remote sensing, industrial inspection, document understanding and public-sector search, with Deutscher Wetterdienst and Enpal among the named clients.

What the portfolio rarely states is outcomes: these are engineering write-ups, and several describe systems still in development rather than in production.

The weaker fit is a buyer who needs a business case defended in numbers—this team documents how a model works far better than what it earned.

### Key strengths
- Publishes methods at a level that can be independently assessed—named models, datasets, and the reasoning behind rejected approaches
- Works with open datasets and open publication, including a project on Deutsche Bahn's automated-driving program
- Names the scientists on its projects and runs its own annual conference, so the bench is verifiable before you engage

### Good to know
- Case studies describe method rather than result—ask for a deployed reference with numbers if you need one
- A 10-49 person team taking research-grade work; confirm capacity and timeline before committing to a deadline

## Compared head-to-head
- dida vs Lautmaler: https://www.aiagencies.eu/compare/dida-vs-lautmaler
- dida vs Ultra Tendency: https://www.aiagencies.eu/compare/dida-vs-ultra-tendency
- dida vs Faculty: https://www.aiagencies.eu/compare/dida-vs-faculty-ai

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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