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Invisible Collector helps companies improve their financial health by disrupting the $67,2 Billion debt collections market without using a single collector




Payments & Collections




Early/Mid Stage


  • Problem
    In order to manage debt files companies rely on internal/external solutions based on skilless workforce, paid at minimum wage, that perform one-way basic management ( pay me; pay me; pay me) under the creditors name. This poor service damages brand reputation as well as the result of the operation. To be successful it is important to create the perfect environment to match creditors with the best professionals available in order to deliver the perfect solution to our clients.
  • Product or service
    We are building an AI collections platform that incorporates human collectors to solve hard disputes. By doing this you get the perfect collections solution with a sophisticated approach combining big data processing, behavioral analytics and all of it with 10 times less workforce.
  • Market and validation

    We are currently performing debt collections for world-class companies like EDP, Iberdrola or Konica Minolta, and developing innovation projects with BBVA, EDP, Vodafone and Abanca. In total we are performing collections for 126 companies, we have more than $260 million under management and over 2 million debt files.

  • Competition and diferentiation

    Within the scope of tradicional competitors, ignoring several local collection agencies, we can identity 4 multinationals with presence in at least 3 continents with high levels of digitalization: Atradius Collections Servdebt Intrum Justicia Whitestar These entities not only provide debt management services but also buy expired debts, mostly from the finance sector which they later manage. It is within these two business models that we can identify a major concern they have to face after the release of GDPR: they cross-reference debtor information from different client companies using said information later on to help manage their own internal debts creating a second data privacy problem, not to mention a conflict of interests. Enterprise level companies are uncomfortable with the misuse of their data by third parties so they search for partners that can provide a solution compatible with the current data protection legislation. Furthermore these companies often employ rudimentary methods mostly based on - “Pay me!!! Pay me!!! Pay me!!!” - disregarding the condition the debtor has as a client.

  • Business model

    Our Business model is based on a fee per each process managed, which can be a fixed fee at the entry of the debt or a success fee when the debt is collected.

  • Projection

    Financial Projects (€)










    271.000 €

    839.000 €

    2.905.000 €

    7.407.000 €

    18.890.000 €

    29.279.000 €


    199.000 €

    243.000 €

    1.256.000 €

    4.215.000 €

    13.356.000 €

    16.913.000 €

    20.085.000 €


    3.000 €

    28.000 €







We are the right team to do this projetc. We are a mix of software engineers and economists that have vast experience in machine learning, behavioral economics and debt collections.  

Team members

Miguel Rangel


Miguel Rangel, economist and former International Collections Manager of a credit insurance company in Madrid, currently running the financial department of Invisible Cloud

Pedro Mendes


Pedro Mendes, Software engineer and former FEUP professor, created 2 startups which eventually sold (one of them to Telefonica in Chile) before start Invisible Cloud

João Madureira


João Madureira, Software engineer and former Senior Developer in some multinational like Nokia Siemens and Betfair, currently running the IT department of Invisible Cloud

José Carlos Alves

Business Developer

José Carlos Alves, Economist Former Business Developer for the US in a major Portuguese bank, now leading the international business development process of Invisible Cloud.

Diogo Mendes


Diogo Mendes, Economist and Behavioral Economics specialist with academic papers published, currently working in behavioral patterns along with the IT department

Luis Silva

Software Engineer

Luis Silva, Software engineer specialized in data integration

Adalberto Alves

Software Engineer

Adalberto Alves, Software engineer specialized in software architecture

Sergio Antunes


Sergio Antunes, mentor for business model adaptation to the Utilities sector

Ricardo Ribeiro


Ricardo Ribeiro, mentor for business model adaptation to the Financial sector

Project documents

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Pitch deck Download

Invisible Collector desarrollará proyecto innovador en España gracias a Rising UP in Spain de ICEX

Invisible Collector desarrollará proyecto innovador en España gracias a Rising UP in Spain de ICEX

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