At a glance

FUSS is an AI-powered app designed to help busy professionals discover the best leisure spots through curated, personalised recommendations.

• Hyper personalised recommendations that are suited to the users behavioural trends and inputted interests.

Quick fire details

• Headquarters: London, UK

• Employee count: 4

• Business model: B2B2C

• Backed by: £200,000. Backed by 3 angels, 1 family office, ULTRA.VC.

• Funding amount: Raising £400,000 pre-seed

The founder

• Cofounder and CEO, Daria Berman: Ex UCL and High School of Economics (neurobiology & behavioural economics and corporate finance). 13 relocations to date.

• Cofounder and CPO, Anastasiia Kanygina: Ex Chief Project Manager Commersart and UPTOWN. 13 relocations to date.

The market

• Global mobility is becoming far more popular as the world becomes more connected there’s been a steady increase in people relocating internationally.

This startup is solving one key problem:

• Places discovery is broken with incumbents offering limited customisation, cognitive overload and lack of trust.

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Traction metrics

• 55k+ downloads and $200k ARR (business client rev) in Eastern Europe, and recently launched in London

• 20% Month 5 retention rate,

• 1500 MAU (organically)

• UK Innovator Founder endorsement and ULTRA.VC accelerator programme.

Competitive landscape

• FUSS operates in a crowded market of leisure and discovery platforms, with indirect competitors like TripAdvisor, Google Maps, and TikTok, and more focused players like DOJO, Design My Night, Fever, and OpenTable.

  • FUSS is the first to combine personalisation and decision-making. e.g. curation-first, context aware and built for a generation that relocates.

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Our Take

Appeals

• Curation-first: Only the top 25% of venues make it into the app. Vetting venues significantly reduces the decision overload users currently experience (reducing churn).

• Context-aware: FUSS’s AI allows for recommendations to be tailored to mood, occasion, and group dynamics. Personalisation is a huge step from current wide sorting features.

• Plugging the gap: The FUSS differentiators positions them not just as a discovery tool, but as a decision engine — the missing layer in the leisure stack.

Risks

• Monetisation: FUSS’s current monetisation model depends on partner venues paying for traffic and visibility. May take time to scale or prove ROI at volume. Currently, testing performance-based models with pilot partners.

• Competition: The local discovery space is crowded. Particularly the behaviour on social media with finding popular venues and experiences.

• Frequency: Leisure discovery is a low-frequency use case for many users. FUSS are focusing on high-intent moments like group planning and social outings, which increase repeat usage.

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