# dida vs Faculty

> Comparing AI Consulting options: Berlin vs London. 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/dida-vs-faculty-ai
Generated: 2026-09-20 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | dida | Faculty |
|---|---|---|
| Editorial score | 68/100 | 73.3/100 |
| Hourly rate | €130-175/hr (directory-listed) | not published |
| Team size | 10-49 | — |
| Location | Berlin, Germany | London, United Kingdom |
| Founded | 2018 | 2014 |
| Last reviewed | 2026-08-04 | 2026-08-04 |
| Services | AI Consulting, AI Development, AI Automation | AI Consulting, AI Development, AI Automation, AI Agents, Generative AI |
| Industries | Manufacturing, Public Sector, Legal | Public Sector, Healthcare, Legal, FinTech |

## Our take on dida
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

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

Published case studies show systems that survived scrutiny most agencies never face—the NHS AI Lab's model-validation process for clinical AI, deployed generative-AI tooling inside Tide's support operation, and an LLM-backed recommender that now sources 25% of Axiom Law's hires.

The published work index spans defense, energy, insurance, and government, though many entries are anonymized or brief.

Rates are not published and the engagement profile is institutional—expect procurement-grade process rather than a lightweight pilot.

The weaker fit is a small team wanting a fast, inexpensive proof of concept; this bench is built for work where failure has consequences.

### Key strengths
- Published 10% ticket-handling-time reduction at Tide, with the deployed tools named (AgentAssist, MemberSummarise on Amazon Bedrock)
- Applied AI since 2014 with an in-house fellowship talent pipeline—delivery here predates the LLM wave
- NHS England's deputy director of AI is quoted crediting the validation work in the published study

### Good to know
- Much of the work index is anonymized (military, challenger bank)—ask for a speakable reference in your sector
- No published rates; budget for institutional procurement timelines

## Which to choose
Choose dida for:
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in Manufacturing

Choose Faculty for:
- closer collaboration at a smaller team scale
- disclosed specialization in Healthcare / FinTech
- broader service offering — also covers AI Agents and Generative AI
- a higher overall editorial score

## Questions buyers ask

### Which has more Public Sector experience, dida or Faculty?
Both agencies show documented Public Sector work. Faculty has the broader industry stack overall, with disclosed experience in Healthcare, FinTech beyond their shared focus.

### Which scores higher overall, dida or Faculty?
Faculty scores 73.3/100, 5.3 points higher than dida at 68/100. See our methodology for how scores are calculated.

### When should I consider both dida and Faculty?
Consider running parallel discovery briefs with dida and Faculty 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