Change sentiment of a single word
The models used for sentiment analysis are generally the result of a machine-learning process. You can produce your own model by running the model creation on a training set where the sentiments are tagged the way you like, but this is a significant undertaking, especially if you are unfamiliar with the underpinnings.
For a quick and dirty fix, maybe just make your code override the sentiment for an individual word, or (somewhat more challenging) figure out how to change its value in the existing model.  Though if you can get a hold of the corpus the NLTK maintainers trained their sentiment analysis on and can modify it, that's probably much simpler than figuring out how to change an existing model.  If you have a corpus of your own with sentiments for all the words you care about, even better.
In general usage, "quick" is not superficially a polarized word -- indeed, "quick and dirty" is often vaguely bad, and a "quick assessment" is worse than a thorough one; while of course in your specific context, a service which delivers quickly will dominantly be a positive thing. There will probably be other words which have a specific polarity in your domain, even though they cannot be assigned a generalized polarity, and vice versa -- some words with a polarity in general usage will be neutral in your domain. Thus, training your own model may well be worth the effort, especially if you are exploring utterances in a very specific register.
I've been working with NLTK in Python for a few days for sentiment analysis and it's a wonderful tool. My only concern is the sentiment it has for the word 'Quick'. Most of the data that I am dealing with has comments about a certain service and MOST refer to the service as being 'Quick' which clearly has Positive sentiments to it. However, NLTK refers to it as being Neutral. I want to know if it's even possible to retrain NLTK to now refer to the Quick adjective as having positive annotations?