Different NLP applications have different efficiency constraints (i.e. quality of the results and throughput) that reflect on each core linguistic component. Syntactic processors are basic modules in some NLP application. A customization that permits the performance control of these components enables their reuse in different application scenarios. Throughput has been commonly improved using partial syntactic processors. On the other hand, specialized lexicons are generally employed to improve the quality of the syntactic material produced by specific parsing (sub)process (e.g. verb argument detection or PP attachment disambiguation) . Building upon the idea of grammar stratification, in this paper a method to push modularity and lexical sensitivity, in parsing, in view of customizable syntactic analysers is presented. A framework for modular parser design is proposed and its main properties are discussed. Parsers (i.e. different parsing module chains) are then presented and their performances are analyzed in an application-driven scenarios.