  Qualitative research studies often employ a contextual inquiry, or a field
study that involves in-depth observation and interviews of a small sample of
study participants, in-situ, to gain a robust understanding of the reasons and
circumstances that led to the participant's thoughts, actions, and experiences
regarding the domain of interest. Contextual inquiry, especially in sensitive
data studies, can be a challenging task due to reasons such as participant
privacy, as well as physical constraints such as in-person presence and manual
analysis of the qualitative data gathered. In this work, we discuss Enqu\^ete
Contextuelle Habile Ordinateur (ECHO); a virtual-assistant framework to
automate the erstwhile manual process of conducting contextual inquiries and
analysing the respondents' subjective qualitative data. ECHO automates the
contextual inquiry pipeline, while not compromising on privacy preservation or
response integrity. Its adaptive conversational interface enables respondents
to provide unstructured or semi-structured responses in free-form natural
language, allowing researchers to explore larger narratives in participant
response data. It supports response-driven exploratory questions and automates
coding methodologies for qualitative data, thus enabling the inquirer to dive
deeper into correlated questions and to do better cause-effect analysis. It
focuses on addressing the limitations of manual annotation, bringing
standardisation to free-form text, and eliminating perspective bias amongst
different reviewers of subjective responses. A participatory mental health
study was conducted on 167 young adults bifurcated into two focus groups; one
of which was administered a conventional contextual inquiry, and the other via
ECHO, virtually. ECHO outperformed on participant transparency, response detail
and median time required for end-to-end inquiry completion, per participant.
