No. There are several reasons why daptics cannot offer such a comforting guarantee, among which:
Experimental noise may wash out any structure available for optimization. Experimental noise should be comfortably beneath 10% for daptics to do its job. Experimental noise may be reduced by performing replicates, and averaging response of replicates; the noise goes down as √N, where N is the number of replicates.
The response surface may be too complex for the experimental bandwidth available. Very complex response surfaces require more data.
This depends on your experimental setup. You want to try and include all parameters that are important, in the sense that they could seriously affect the experimental response. daptics can handle 4-20 parameters, depending on the roughness of the response landscape and your experimental bandwidth. If you have fewer than 4 parameters, you might try simpler experimental design approaches, e.g. sweeping parameters.