DEXi supports two basic tasks:

- the development of qualitative multi-attribute models;
- the application of models for the evaluation and analysis of options.

The models are developed by defining:

- attributes: qualitative variables that represent decision subproblems,
- scales: ordered or unordered sets of symbolic values that can be assigned to attributes,
- tree of attributes: a hierarchical structure representing the decomposition of the decision problem,
- utility functions: rules that define the aggregation of attributes from bottom to the top of the tree of attributes.

In the evaluation and analysis stage, DEXi facilitates:

- description of options: defining the values of basic attributes (terminal nodes of the tree),
- evaluation of options: a bottom up aggregation of option values based on utility functions,
- analysis of options: what-if analysis, "plus-minus-1" analysis, selective explanation, option generation and comparison of options,
- reporting: graphical and textual presentation of models, options and evaluation results.

DEXi differs from most conventional multi-attribute decision modeling tools in that
it uses qualitative (symbolic) attributes instead of quantitative (numeric) ones.
Also, aggregation (utility) functions in DEXi are defined by *if-then* decision
rules rather numerically by weights or some other kind of formula.
(However, DEXi does support weights indirectly.)

In comparison with its predecessor DEX, DEXi has a more modern and more convenient user interface. Also, it has better graphical and reporting capabilities, and facilitates the use of weights to represent and assess qualitative utility functions. On the other hand, DEXi is somewhat less powerful than DEX in dealing with incomplete option descriptions: DEX employs probabilistic and fuzzy distribution of values, while DEXi facilitates only the use of crisp or unknown option values.