Comparison updated · Newest review
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 | Datatonic | Faculty |
|---|---|---|
| Editorial score | Higher score: 74.8/100 | 73.3/100 |
| Hourly rate | Contact for rates | Contact for rates |
| Team size | Larger team: 50-249 | |
| Location | London, United Kingdom | London, United Kingdom |
| Founded | 2013 | 2014 |
| Last reviewed | More recent review: Sep 7, 2026 | Aug 4, 2026 |
What we said about each
Our take on Datatonic
The architecture detail is unusually specific for a consultancy site. The AstraZeneca engagement documents a Bedrock RAG pipeline that ingested more than 20,000 scientific documents and 1. 5 billion input tokens, with a custom batch-sync engine written to get around a 1,000-document platform ceiling.
Reviewed Sep 7, 2026
Read the full assessment →Our take on Faculty
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 Datatonic if you need
- larger team capacity for multi-stream or enterprise-scale programs
- disclosed specialization in E-commerce
Choose Faculty if you need
- closer collaboration at a smaller team scale
- disclosed specialization in Public Sector / Legal
- broader service offering — also covers AI Automation and AI Agents
Strengths and watch-outs
Both upsides and risks, straight from our editorial assessments.
Datatonic
Strengths
- The AstraZeneca RAG pipeline is documented at 20,000+ documents and 1.5 billion input tokens with the architecture published in full
- Vodafone AI Booster runs in more than eight markets and cut proof-of-concept-to-production by about 80%; its MLOps templates were open-sourced
- Delivery on both Google Cloud and AWS Bedrock, with named Datatonic engineers quoted alongside client-side leads
Watch-outs
- The AstraZeneca and Alpian write-ups quantify engineering, not business outcome—ask for the return figure
- Much of the work sits inside Google Cloud partnerships—ask who owns the platform relationship and what it costs after handover
- Seven offices across Europe and Toronto—ask which one staffs your engagement
Faculty
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.
Industry coverage
Where the two overlap, and where each covers ground the other does not.
Frequently asked questions
- Which has more FinTech experience, Datatonic or Faculty?
- Both agencies show documented FinTech work. Faculty has the broader industry stack overall, with disclosed experience in Public Sector, Legal beyond their shared focus.
- Which scores higher overall, Datatonic or Faculty?
- Datatonic scores 74.8/100, 1.5 points higher than Faculty at 73.3/100. Both portfolio quality and business credibility feed into the score — see our methodology for the full breakdown.
- When should I consider both Datatonic and Faculty?
- Consider running parallel discovery briefs with Datatonic and Faculty 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.