A profile of Denmark-based independent consumer review platform Trustpilot, which has raised €162M and has 65M reviews of businesses worldwide
With 65m reviews, Trustpilot is learning to spot the fakes and fight back with the help of algorithms. — Would you ever book an Airbnb stay without checking the scores of the host? Tweets: @mimibilling and @mstothard Tweets: Mimi Billing / @mimibilling : If you would do anything to get a 5-star-rating on @Trustpilot: “The thing is that the more you are trying to manipulate your scores, the easier it is for us to detect,” @Peter_Muhlmann tells @Siftedeu. #nordicmade #cphftw @DraperEsprit @northzoneVC https://sifted.eu/... Michael Stothard / @mstothard : With 65m reviews, Trustpilot is learning to spot the fakes and fight back with the help of algorithms. https://sifted.eu/...
Context & Ripple Effects
This 2019 profile lands mid-arc for Trustpilot: after a $73.5M Series D in 2015, the company has raised €162M in total and accumulated 65M reviews, and CEO Peter Muhlmann frames its next problem as integrity rather than scale. The mechanism he describes — manipulators leaving patterns that are easy to spot — places Trustpilot alongside [[a:940377|fraud-detection services like Sift and SecureAuth that score user trustworthiness from opaque signal sets]].
The profile also foreshadows the company's central tension: Trustpilot earns subscriptions from the very businesses it hosts reviews about, the same dynamic a reviewer would later weaponize when short seller Grizzly attacked the London-listed company over alleged pressure to pay. The path from this profile runs through the £473M London IPO to that confrontation, making this the moment where the detection-vs-monetization trade-off first gets articulated.
First-order effects
- Businesses on Trustpilot now face algorithmic scrutiny of their review patterns — per Muhlmann, the harder a company tries to manipulate its score, the more detectable the attempt becomes.
Second-order effects
- The subscription business model built on top of reviewed companies creates a structural conflict of interest: paying customers and policed users are the same population, which is exactly the seam Grizzly would later exploit publicly.
Third-order effects
- Review platforms drift toward becoming trust-infrastructure vendors — selling verification and fraud signals as products — which raises the stakes on their neutrality and invites the kind of adversarial financial and regulatory scrutiny that followed the IPO.
The trend: Consumer-review platforms are shifting from passive repositories into active trust-enforcement businesses, where algorithmic fake detection becomes both the core product and the source of their credibility conflicts.