# ML6 vs Netcompany

> Comparing AI Consulting options: Ghent vs Copenhagen. 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/ml6-vs-netcompany
Generated: 2026-09-20 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | ML6 | Netcompany |
|---|---|---|
| Editorial score | 74.8/100 | 74.8/100 |
| Hourly rate | not published | not published |
| Team size | 50-249 | 250+ |
| Location | Ghent, Belgium | Copenhagen, Denmark |
| Founded | — | 2000 |
| Last reviewed | 2026-08-04 | 2026-09-20 |
| Services | AI Consulting, AI Development, AI Agents, Generative AI | AI Development, Generative AI, AI Consulting |
| Industries | FinTech, Manufacturing, Public Sector | Public Sector, FinTech |

## Our take on ML6
Reviewed 2026-08-04 by Gabor Kiss against published evidence.

The published case library runs to 34 studies, and the ones read in full carry before-and-after numbers rather than adjectives—Scout24's HeyImmo assistant moved user satisfaction from 68% to 75% and first-token latency from 8 seconds to under 5, and Syngenta's lab-inspection vision model doubled analysis speed across half a million wells a year.

Studies name the stack down to the component (OpenAI direct API, Pydantic agents, DataDog on Scout24's own AWS; Vertex AI for Syngenta) and describe how the client was left able to run the system without them.

Work spans regulated and public buyers—the national cyber security center, the EU Council newsroom, Flemish highways—alongside industrial names.

The weaker fit is a buyer wanting a fixed quote up front: no rates, headcount, or founding year appear anywhere public.

### Key strengths
- Case studies publish client-side quotes from named engineering managers, not marketing testimonials
- Published outcomes at the national cyber security center include 3x faster vulnerability handling and 70% faster NIS2 response
- Both major clouds are in evidence—AWS and Google Vertex AI appear in separately documented builds

### Good to know
- Two energy studies run anonymized as "Energy Company"—ask for a speakable reference if you need one in that sector
- No published rates, team size, or founding year; anchor with the European range on our rate index and ask who staffs the work
- Delivery sits across four offices—confirm which one your team would come from

## Our take on Netcompany
Reviewed 2026-09-20 by Gabor Kiss against published evidence.

Three named AI systems are documented as running rather than piloted: DSB's train-delay swarm, a model per railway line orchestrated by the firm's PULSE platform, which predicts arrival to within three minutes at 95 percent accuracy; Topdanmark's TopGPT, which masks personal data on locally hosted models before a retrieval step reads the customer's own policy, live around the clock since April 2024 and past 80,000 conversations by that November; and Copenhagen Airport's luggage-timestamp model, in production since 2016 and retrained whenever it drifts.

The AI work is a minority of what the firm publishes—the case index is dominated by large public-sector and enterprise IT delivery for the European Commission, NHS England and the UK Ministry of Defence—and the European Commission's FERMI disinformation tool has completed three police pilots on live data rather than a full deployment.

What raises confidence is what the write-ups admit: the Copenhagen Airport study records the queue-prediction model that never reached production beside the two that did, so ask for the same candor about your own use case.

The weaker fit is a buyer wanting one scoped model from a small team—this is a listed group of more than 9,500 people that builds on its own platforms and expects to operate what it builds, so settle minimum engagement size and account staffing before scoping.

### Key strengths
- Case studies name the firm's own engineers—Mathias Engel on the DSB swarm, Sine Abildgaard Rosenberg on Copenhagen Airport's hazard models—so you can see who built the work, not only who sold it
- Operational detail is published rather than implied: the airport's hazard model is re-evaluated every six months, and TopGPT is audited continuously through a generative-AI evaluation framework
- Facts check out away from the site: Netcompany Group A/S is a listed Danish company, CVR 39488914, founded in Copenhagen in 2000 and reporting 9,500-plus staff across more than 10 countries

### Good to know
- Nothing published indicates cost or minimum engagement size—ask for a commercial frame early, since the documented work runs in years and at national scale (Copenhagen Airport 2015–2023)
- The three AI systems carrying figures are all Danish; if your brief sits in another country, ask which local team delivered the closest comparable and ask to speak to it
- Delivery runs on the firm's own platforms—PULSE, EASLEY AI, PERSEUS—so confirm what you would owe operationally, and on what terms, if you later move off them

## Which to choose
Choose ML6 for:
- closer collaboration at a smaller team scale
- disclosed specialization in Manufacturing
- broader service offering — also covers AI Agents

Choose Netcompany for:
- larger team capacity for multi-stream or enterprise-scale programs
- an overall editorial score of 74.8/100

## Questions buyers ask

### Which has more FinTech experience, ML6 or Netcompany?
Both agencies show documented FinTech work. ML6 has the broader industry stack overall, with disclosed experience in Manufacturing beyond their shared focus.

### Which scores higher overall, ML6 or Netcompany?
Both score equally well overall (74.8/100). The deciding factor is specialization — see the editorial quotes and Strengths sections above. Full methodology.

### When should I consider both ML6 and Netcompany?
Consider running parallel discovery briefs with ML6 and Netcompany 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