Agency Comparison
appliedAI vs Faculty AI
Comparing AI Consulting options: Munich vs London.
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 | Faculty AI |
|---|---|---|
| Editorial score | 64.3/100 | Higher score: 73.3/100 |
| Hourly rate | Contact for rates | Contact for rates |
| Team size | ||
| Location | Munich, Germany | London, United Kingdom |
| Founded | — | 2014 |
| 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 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 →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 Manufacturing
- an overall editorial score of 64.3/100
Choose Faculty AI if you need
- disclosed specialization in Public Sector / Legal
- broader service offering — also covers AI Automation
- 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
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
Service coverage
Where the two overlap, and where each covers ground the other does not.
Only appliedAI
None.
Only Faculty AI
Industry coverage
Where the two overlap, and where each covers ground the other does not.
Frequently asked questions
- Which has more Healthcare experience, appliedAI or Faculty AI?
- Both agencies show documented Healthcare work. Faculty AI has the broader industry stack overall, with disclosed experience in Public Sector, Legal, FinTech beyond their shared focus.
- Which scores higher overall, appliedAI or Faculty AI?
- Faculty AI 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 Faculty AI?
- Consider running parallel discovery briefs with appliedAI and Faculty AI 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.