# element61 vs ML6

> Two top-rated AI Consulting agencies in Ghent. 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/element61-vs-ml6
Generated: 2026-09-17 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | element61 | ML6 |
|---|---|---|
| Editorial score | 65.8/100 | 74.8/100 |
| Hourly rate | not published | not published |
| Team size | 50-249 | 50-249 |
| Location | Ghent, Belgium | Ghent, Belgium |
| Founded | 2007 | — |
| Last reviewed | 2026-09-10 | 2026-08-04 |
| Services | AI Consulting, AI Development, Generative AI | AI Consulting, AI Development, AI Agents, Generative AI |
| Industries | FinTech, Manufacturing, Public Sector | FinTech, Manufacturing, Public Sector |

## Our take on element61
Reviewed 2026-09-10 by Gabor Kiss against published evidence.

This is a large analytics consultancy with an AI practice inside it, and the firm publishes the numbers most agencies leave out: more than 145 consultants averaging nine years of experience, over €28 million of 2025 revenue, individual consultant CVs on the site, and group ownership under Moore Belgium since 2016.

The AI delivery evidence is narrower than that footprint suggests but it is real and dated. At the roofing and cladding company Modde, machine-learning models rank prospects, predict which orders convert to invoices and segment customers, surfaced to sales representatives through a Qlik app with write-back over Databricks, live since March 2025.

What those write-ups do not carry is an outcome figure, so you will be judging approach and staffing rather than measured effect.

Best for Belgian enterprises and public bodies that want AI built into an existing reporting and performance-management estate by a consultancy their finance function will recognize.

### Key strengths
- Verifiable at an unusual level for this category: published headcount, average consultant experience, annual revenue and named CV pages for individual consultants
- Vendor coverage spans IBM, Microsoft, Databricks, SAP, CCH Tagetik, Vena and Qlik, so an architecture recommendation is not forced by a single partnership
- The Modde build shows AI landing where the business already works—models feeding a reporting app with write-back, rather than a separate tool nobody opens

### Good to know
- The published project library is dominated by business intelligence and performance management; ask specifically for AI engagements from the last two years and what each one does today
- AI case studies name the technique but not the result—ask what changed commercially at Modde since go-live and who maintains the models now
- Delivery is described as coaching and co-development, so budget for your own people's time; this model assumes your team takes the system over

## 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

## Which to choose
Choose element61 for:
- an overall editorial score of 65.8/100

Choose ML6 for:
- broader service offering — also covers AI Agents
- a higher overall editorial score

## Questions buyers ask

### Which has more FinTech experience, element61 or ML6?
Both agencies show documented FinTech work and have similar industry breadth. Compare directly on the agency profiles: element61 and ML6.

### Which scores higher overall, element61 or ML6?
ML6 scores 74.8/100, 9 points higher than element61 at 65.8/100. See our methodology for how scores are calculated.

### When should I consider both element61 and ML6?
Consider running parallel discovery briefs with element61 and ML6 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