# ML6 vs Satalia

> Comparing AI Consulting options: Ghent 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/ml6-vs-satalia
Generated: 2026-09-17 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | ML6 | Satalia |
|---|---|---|
| Editorial score | 74.8/100 | 74.8/100 |
| Hourly rate | not published | not published |
| Team size | 50-249 | 50-249 |
| Location | Ghent, Belgium | London, United Kingdom |
| Founded | — | 2008 |
| Last reviewed | 2026-08-04 | 2026-09-07 |
| Services | AI Consulting, AI Development, AI Agents, Generative AI | AI Consulting, AI Development, AI Agents |
| Industries | FinTech, Manufacturing, Public Sector | E-commerce, FinTech, Manufacturing |

## 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 Satalia
Reviewed 2026-09-07 by Gabor Kiss against published evidence.

Two of the systems here have been running for a decade: the Tesco last-mile engine built in 2015 still schedules 100,000 deliveries a day, and the PwC workforce optimizer built in 2016 still assigns 4,200 people across 6,600 client engagements.

The newer work carries numbers too—DFS reports 18% better fuel efficiency and 19% less unplanned driver overtime, and a UK telco engagement documents £59 million in savings with 139 full-time staff working from the model's output.

The specialty is optimization and operations research rather than generative AI: more than 20 PhDs and MScs in discrete mathematics sit behind delivery, and the founding team includes the UK government's Chief Scientific Adviser for National Security.

Since the 2021 WPP acquisition the published investment has gone into marketing AI products, so a buyer with a routing or scheduling problem should ask which team still does that work.

### Key strengths
- The Tesco delivery system built in 2015 still schedules 100,000 deliveries a day; the 2016 PwC workforce optimizer still assigns 4,200 people to 6,600 engagements
- More than 20 PhDs and MScs in discrete mathematics and operations research, with founders from UCL Computer Science and the UK national security advisory role
- DFS and the UK telco engagements both publish outcome numbers: 18% fuel efficiency, £59M savings, 200x return on investment

### Good to know
- WPP has owned Satalia since 2021 and recent published work is marketing AI—ask who staffs an optimization engagement now
- The strongest results are nine and ten years old—ask for a routing or scheduling system delivered in the last three years
- No rate and no engagement model are published—ask whether they sell projects, embedded teams or platform licenses

## Which to choose
Choose ML6 for:
- disclosed specialization in Public Sector
- broader service offering — also covers Generative AI

Choose Satalia for:
- disclosed specialization in E-commerce
- an overall editorial score of 74.8/100

## Questions buyers ask

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

### Which scores higher overall, ML6 or Satalia?
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 Satalia?
Consider running parallel discovery briefs with ML6 and Satalia 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