# Dear Future vs Found

> Comparing AI SEO options: Copenhagen 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/dear-future-vs-found
Generated: 2026-09-24 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | Dear Future | Found |
|---|---|---|
| Editorial score | 61.8/100 | 55/100 |
| Hourly rate | not published | €85-130/hr (directory-listed) |
| Team size | 50-249 | 50-249 |
| Location | Copenhagen, Denmark | London, United Kingdom |
| Founded | 2005 | 2005 |
| Last reviewed | 2026-09-09 | 2026-09-23 |
| Services | AI SEO, AI Consulting, AI Development | AI SEO, AI Marketing, AI Automation |
| Industries | E-commerce, SaaS & B2B | E-commerce, FinTech, SaaS & B2B |

## Our take on Dear Future
Reviewed 2026-09-09 by Gabor Kiss against published evidence.

This is the most measured portfolio in the Copenhagen roster. For Bonnier Publications, customer data from the CRM, Google Analytics and the newsletter platform was consolidated into BigQuery, a purchase-propensity model segmented 33.3 million users into five groups, and the segments were activated directly in search and display: 53 percent growth in digital subscription sales, 18 percent higher conversion, 327 million predictions generated.

The pattern repeats at smaller scale. Atrium's course-demand model predicts registrations three months out from web activity joined to CRM and booking data, returning 45 percent better marketing performance and two to three hours a day off manual monitoring. A clinically trained chatbot for the addiction treatment center Tjele produced 354 conversations and 25 qualified calls in three months, a 14 percent conversion, against telephone inquiries falling 20 to 30 percent.

The Pandora engagement is the honest counterweight: the client had already built a customer-lifetime-value model that its national marketing teams were not using, and the work was activation and roadmapping rather than modeling.

The weaker fit is a buyer who needs a model built from research rather than from commercial data already in the stack.

### Key strengths
- Outcomes are published with the mechanism attached—33.3 million users into five segments, 327 million predictions, 53 percent subscription growth for Bonnier Publications
- The data path is named on each engagement: CRM, analytics and platform data consolidated into BigQuery before any model is trained
- Twelve case studies, every one with a named client, spanning media, retail, healthcare and education

### Good to know
- The work sits on commercial first-party data—if your problem needs a model trained on something else, ask for that engagement specifically
- The Pandora case is activation of a model the client already had; check which of your candidates' studies are modeling and which are adoption
- Trading since 2005 under a former name and rebranded recently—confirm which team carried over and who would staff you

## Our take on Found
Reviewed 2026-09-23 by Gabor Kiss against published evidence.

Found's clearest AI work is a model it built: for Secret Sales, a deep neural network predicting each product's 30-day sales value, served to the client through a web app and rolled out on a small slice of budget first, credited with 28% higher ROAS than the account's other Shopping campaigns and more than £1M in incremental profit.

The other AI-tagged studies are thinner on the AI itself—1st Central's award-winning acquisition work runs on Google's pre-built audiences and lookalike modeling, and the Clermont Hotel Group project mined Instagram posts into a "hidden gems" ranking and used generative AI to draft the marketing plan.

AI search is the biggest part of the pitch, run through the in-house Luminr platform, yet no case study reports a citation or AI-answer result; the Luminr study for Taylor & Francis counts keywords analyzed and snippet opportunities found, not an outcome.

The weaker fit is buyers who want AI-search results they can check before signing, or an AI system outside marketing—every study read sits inside paid media, SEO or campaign planning.

### Key strengths
- Secret Sales documents a model built and put to work: a neural network scoring products by predicted 30-day sales value, a client web app, and a rollout that tiered bidding by predicted potential
- Client executives named with titles on the AI work—Secret Sales' founder and CEO, 1st Central's Digital & Marketing Director, Clermont's Digital Marketing Manager, Haleon's Global Search Lead
- A named Chief AI Officer on the record (BBC Radio 5 Live and Radio 4, The Drum Podcast), and a UK Search Award for Best Use of Search in B2C cited on the 1st Central study

### Good to know
- No published case study reports an AI-search outcome—ask for the citation or share-of-answer movement Luminr recorded for an existing client, with the engines and prompt set it measured
- The data and AI work is credited to sister company Braidr, whose managing director is Found's Chief AI Officer—ask which entity contracts, who builds the model and who owns it afterward
- The AI SEO page calls Found "the #1 AI search agency" with no measurement behind the ranking—treat it as marketing copy and ask for evidence from your own category

## Which to choose
Choose Dear Future for:
- broader service offering — also covers AI Consulting and AI Development
- a higher overall editorial score

Choose Found for:
- disclosed specialization in FinTech
- broader service offering — also covers AI Marketing and AI Automation

## Questions buyers ask

### Which has more E-commerce experience, Dear Future or Found?
Both agencies show documented E-commerce work. Found has the broader industry stack overall, with disclosed experience in FinTech beyond their shared focus.

### Which scores higher overall, Dear Future or Found?
Dear Future scores 61.8/100, 6.8 points higher than Found at 55/100. Both portfolio quality and business credibility feed into the score — see our methodology for the full breakdown.

### When should I consider both Dear Future and Found?
Consider running parallel discovery briefs with Dear Future and Found 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