Anthropological research generally centres on singular researchers in the field. Collaboration typically focuses on the role of the researcher plus their participants, research assistants, gatekeepers or helpers (e.g. in Participatory Action Research). Literature exploring collaborative working of research teams is sparse. Nevertheless, teamwork, often in multidisciplinary settings, is the reality of many qualitative researchers and anthropologists working on large medical or international development projects. In this paper, we reflect on the implications of working as a qualitative, international remote team for knowledge-making. What may be lost in our scientific enquiry when we work this way, and what may we gain? How do we (co-)construct (anthropological) knowledge in this process? Our UK-Malian qualitative remote-team (Anthropologists, Sociologists, Public Health researchers) jointly carried out the qualitative process evaluation of the MaaCiwara project, a randomised control trial in Mali (2021-24), evaluating a low-cost, community-level, behaviour-change intervention to reduce diarrhoea and malnutrition in children, run by Malian and UK university partners. Challenges to our work included the loss of subtleties in meanings or mistranslations (we worked with Bambara, French, English) and the difficulty for the UK team to understand the data sufficiently to contribute to its interpretation and analysis without the immersion, context and understanding associated with longer-term ethnographic fieldwork. This was exacerbated by the loss of subtleties of meanings and mistranslations of audio recordings (transcribed into French from Bambara and translated into English). There were also questions about relinquishing and sharing control over data collection and analysis. In terms of positive impact, however, our knowledge-making benefited from constant creative discussions, drawing on our collective experiences and epistemologies, which then guided the enquiry of our field-team in an interactive process and supported data analysis. This way of generating scientific knowledge may also have much to offer in terms of power-sharing and de-colonizing knowledge production.