Comparison updated August 2026
Side by side
Published data on both agencies. Where one side measurably differs, the dot marks it: the higher score, the lower published rate, the larger team, the more recent review. Which of those is an advantage depends on your brief.
| Attribute | appliedAI | ML6 |
|---|---|---|
| Editorial score | 64.3/100 | Higher score: 73.3/100 |
| Hourly rate | Contact for rates | Contact for rates |
| Team size | ||
| Location | Munich, Germany | Ghent, Belgium |
| Founded | — | — |
| Last reviewed | Aug 4, 2026 | Aug 4, 2026 |
What we said about each
Our take on appliedAI
The Maschinenfabrik Reinhausen build is the one to read: a large language model on a skills-based architecture over a RAG knowledge base, in production, publishing roughly 45x faster Q&A, 1500x on automated extraction and 90% accuracy on base-analysis extraction. The published client list runs to Roche, Linde, Nokia, Vonovia and Giesecke+Devrient, and the G+D write-up tracks a named enterprise from "experimenting" to "practising" over two years rather than claiming a single win. Much of the…
Reviewed Aug 4, 2026
Read the full assessment →Our take on ML6
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…
Reviewed Aug 4, 2026
Read the full assessment →Best for
Where each agency measurably leads on the published data — team capacity, declared specializations, editorial scoring, and rates where both publish them. Use these to match an agency to your project priorities.
Choose appliedAI if you need
- disclosed specialization in Healthcare
- an overall editorial score of 64.3/100
Choose ML6 if you need
- disclosed specialization in FinTech / Public Sector
- a higher overall editorial score
Strengths and watch-outs
Both upsides and risks, straight from our editorial assessments.
appliedAI
Strengths
- One of the few listed agencies publishing hard speedup and accuracy figures from a production system
- AI Act and governance work is a stated practice line, not a blog topic
- An agent program with a published three-month path to a production-ready agent
Watch-outs
- Several reference pages describe use cases rather than named client engagements—ask which are live deployments
- No published rates, headcount or founding year; budget for enterprise procurement
ML6
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
Watch-outs
- 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 EU €100-200/h band and ask who staffs the work
- Delivery sits across four offices—confirm which one your team would come from
Service coverage
Both agencies cover the same services — on this axis there is nothing to separate them, so decide on the evidence behind the work rather than its labels.
Industry coverage
Where the two overlap, and where each covers ground the other does not.
Frequently asked questions
- Which has more Manufacturing experience, appliedAI or ML6?
- Both agencies show documented Manufacturing work. ML6 has the broader industry stack overall, with disclosed experience in FinTech, Public Sector beyond their shared focus.
- Which scores higher overall, appliedAI or ML6?
- ML6 scores 73.3/100, 9 points higher than appliedAI at 64.3/100. See our methodology for how scores are calculated.
- When should I consider both appliedAI and ML6?
- Consider running parallel discovery briefs with appliedAI 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.
Read the full reviews
A side-by-side is a starting point. The full assessments carry the portfolio analysis, documented engagements, and the evidence behind every strength and watch-out above.