Agency Comparison
Faculty AI vs ML6
Comparing AI Consulting options: London vs Ghent.
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 | Faculty AI | ML6 |
|---|---|---|
| Editorial score | 73.3/100 | 73.3/100 |
| Hourly rate | Contact for rates | Contact for rates |
| Team size | ||
| Location | London, United Kingdom | Ghent, Belgium |
| Founded | 2014 | — |
| Last reviewed | Aug 4, 2026 | Aug 4, 2026 |
What we said about each
Our take on Faculty AI
Published case studies show systems that survived scrutiny most agencies never face—the NHS AI Lab's model-validation process for clinical AI, deployed generative-AI tooling inside Tide's support operation, and an LLM-backed recommender that now sources 25% of Axiom Law's hires. The published work index spans defense, energy, insurance, and government, though many entries are anonymized or brief. Rates are not published and the engagement profile is institutional—expect procurement-grade…
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 Faculty AI if you need
- disclosed specialization in Healthcare / Legal
- broader service offering — also covers AI Automation
Choose ML6 if you need
- disclosed specialization in Manufacturing
- an overall editorial score of 73.3/100
Strengths and watch-outs
Both upsides and risks, straight from our editorial assessments.
Faculty AI
Strengths
- Published 10% ticket-handling-time reduction at Tide, with the deployed tools named (AgentAssist, MemberSummarise on Amazon Bedrock)
- Applied AI since 2014 with an in-house fellowship talent pipeline—delivery here predates the LLM wave
- NHS England's deputy director of AI is quoted crediting the validation work in the published study
Watch-outs
- Much of the work index is anonymized (military, challenger bank)—ask for a speakable reference in your sector
- No published rates; budget for institutional procurement timelines
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
Where the two overlap, and where each covers ground the other does not.
Industry coverage
Where the two overlap, and where each covers ground the other does not.
Frequently asked questions
- Which has more Public Sector experience, Faculty AI or ML6?
- Both agencies show documented Public Sector work. Faculty AI has the broader industry stack overall, with disclosed experience in Healthcare, Legal beyond their shared focus.
- Which scores higher overall, Faculty AI or ML6?
- Both score equally well overall (73.3/100). The deciding factor is specialization — see the editorial quotes and Strengths sections above. Full methodology.
- When should I consider both Faculty AI and ML6?
- Consider running parallel discovery briefs with Faculty AI 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.