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Nowadays, scientific databases have become the bread-and-butter of particle physicists. These databases must be maintained and checked repeatedly to insure the accuracy of their content. The COMPETE collaboration aims at motivating data maintenance via the interfacing of theory and experiment at the database level. The database concept then needs to be supplemented by a "model-base". Such an object enables one not only to decide what the best description may be, but also to discern what potential problems exist in the data. The systematization of such a cross-fertilization between models and data results in the "object of knowledge" that is the point at which all existing information resources on a given problem could converge. There are many advantages to such a global approach. First of all, the maintenance of a data set is not a static task: it needs to be motivated by physics. The second advantage is that one can have a common testing ground for theories and models. Thirdly, an extensive theoretical database can be used to plan new experiments and to predict various quantities. Finally, as new data come in, one can very quickly decide on their theoretical impact, and hence immediately evaluate the need for new physics ideas. As we want to treat a large amount of data and many models, computer technology constitutes an important part of our activity. We have concentrated on the elaboration of artificial intelligence decision-making algorithms, as well as on the delivery of computer tools for the end-user. Further linkage with existing databases, such as PDG, COMPAS, and HEPDATA is being developed or planned.