# dida vs Hiflylabs

> Comparing AI Consulting options: Berlin vs Budapest. 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-hiflylabs
Generated: 2026-10-21 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | dida | Hiflylabs |
|---|---|---|
| Editorial score | 68/100 | 61.8/100 |
| Hourly rate | €130-175/hr (directory-listed) | not published |
| Team size | 10-49 | 250+ |
| Location | Berlin, Germany | Budapest, Hungary |
| Founded | 2018 | 2015 |
| Last reviewed | 2026-08-04 | 2026-09-03 |
| Services | AI Consulting, AI Development, AI Automation | AI Consulting, AI Development, AI Automation |
| Industries | Manufacturing, Public Sector, Legal | FinTech, Manufacturing, Legal |

## 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 Hiflylabs
Reviewed 2026-09-03 by Gabor Kiss against published evidence.

The named LLM-era work is the reason to read the case studies: Billingo's text-to-SQL and RAG agent went from proof of concept through pilot to full production on OpenAI, Snowflake and OpenSearch with prompt anonymization via MS Presidio, and Erste Bank's agentic RAG knowledge base on LangGraph and Azure AI Search reports 200-plus documents processed and 40% higher answer accuracy.

The older classical work carries the hardest numbers—MOL Group's location-based pricing showed 3.87% gross-margin growth over a control group after six months of business-as-usual operation, and the Arconic root-cause analysis of a decade-old wheel defect ended with the line modified and the defect gone.

Two of the biggest headlines are anonymized (the 99% due-diligence cut at a global investment firm, the 2x fraud-detection accuracy at a European utility) and the Semilab and Adaptive Recognition chatbots are described as proofs of concept, so separate the deployed from the piloted when you ask for references.

The weaker fit is a buyer with a small, self-contained ML task—the studies pair the AI build with data-platform work on Databricks, Azure or Snowflake, and that is the engagement shape the firm is set up for.

### Key strengths
- Stack is listed on every study—LangGraph, MLFlow, Azure AI Search, Databricks Apps, HuggingFace, MS Presidio—so a technical buyer can check the architecture before the first call
- Third-party standing is visible: FT1000 listing, Databricks, Snowflake Select and AWS Select partner tiers, and 50-plus Databricks-certified staff per the homepage
- Named testimonials with names and titles at Catylex, MOL Group, Cirkul, Adaptive Recognition, Semilab and Billingo, and an engineering blog dated from 2020 through August 2026 with named authors

### Good to know
- The 99% analysis-time cut and the 6x ROI belong to anonymized clients—ask for a speakable reference behind each
- The homepage claims 500-plus projects and 200-plus clients against roughly 25 published case studies—ask for the list in your vertical
- Semilab and Adaptive Recognition are named proofs of concept—confirm what has run in production since, and who operates it

## Which to choose
Choose dida for:
- closer collaboration at a smaller team scale
- disclosed specialization in Public Sector
- a higher overall editorial score

Choose Hiflylabs for:
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in FinTech

## Questions buyers ask

### Which has more Manufacturing experience, dida or Hiflylabs?
Both agencies show documented Manufacturing work and have similar industry breadth. Compare directly on the agency profiles: dida and Hiflylabs.

### Which scores higher overall, dida or Hiflylabs?
dida scores 68/100, 6.2 points higher than Hiflylabs at 61.8/100. Both portfolio quality and business credibility feed into the score — see our methodology for the full breakdown.

### Which is faster to engage, dida or Hiflylabs?
Neither publishes lead times, so this is a read on team size rather than a measured answer. dida runs the smaller team, which usually means fewer procurement gates and a shorter path to kickoff. Hiflylabs runs a larger one, which tends to mean more steps but more capacity to start parallel workstreams. Current kickoff availability is the number that actually decides it — ask both.

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