Discerne turns open-ended text into themes, sentiment, evidence and practical next steps.
For organisations with large volumes of open-ended feedback, research data, customer comments or verbatims.
The problem
Teams collect rich comments, reviews and feedback, but raw verbatims are slow to code and hard to turn into decisions.
It takes time to read, tag and compare large volumes of comments.
Generic summaries can miss the reasons, emotions and trade-offs inside the data.
The useful question is not only what people said, but what the organisation should do next.
What Discerne does
Open-ended comments often contain the reasons behind churn, frustration, product friction or research findings. Discerne gives teams a structured view of what people are saying, why it matters and what to do next.
Illustrative product-output example
Example output: Service Quality feedback grouped into sub-themes, sentiment and supporting evidence.
What you get
Outputs are designed to help teams understand, compare, explain and act on open text.
A structured view of the main themes and how they relate to each other.
StructureTheme and sub-theme lists that show what appears most often or matters most for the question.
PrioritisationAn explanation of positive, negative and mixed sentiment, including what is driving it.
Feeling and contextA view of new, rising or recurring concerns that may need attention.
Early signalsStructured rows and selected excerpts that make the evidence easier to review and compare.
EvidenceA concise explanation of the most important findings and practical next steps.
ActionIllustrative product-output example
Three shortened examples showing how comments can become coded rows, evidence excerpts and actions.
Example review excerpt
"The agent was helpful and fixed my query quickly."
Use as supporting evidence for helpful support interactions.
Example review excerpt
"I sent documents but did not receive a response for several days."
Flag response delays as a recurring service-quality issue.
Example review excerpt
"Regular updates kept me informed, and the information was clear."
Capture clear communication as a positive driver to protect.
How it works
Start with the question, then structure the analysis around the decision it needs to support.
Provide the comments, transcripts, reviews or tickets you want to understand.
Clarify audiences, comparisons, categories and the decisions the findings need to support.
Discerne codes the text into themes, sentiment, issues and evidence for interpretation.
You get structured outputs, selected quotes and recommendations written for stakeholders.
Where Discerne helps
For messy, high-value text sources where the detail matters and manual review is difficult.
Analyse complaints, reviews, support tickets and feedback forms to identify recurring friction and improvement priorities.
Structure pulse survey comments, engagement feedback and listening exercises into themes leaders can act on.
Code survey comments, research verbatims, interview transcripts and focus group notes into usable findings.
Group app reviews, user comments, feature requests and support notes into needs, objections and usability themes.
Structure consultation submissions, stakeholder responses and open survey comments into themes and evidence.
Analyse open-ended brand feedback, reviews and campaign comments to understand associations and concerns.
Trust and privacy
Text data can include sensitive customer, employee or research material. Each project begins with clear boundaries for data use, reporting and audience.
Analysis focuses on the questions and decisions the data was gathered to support.
Outputs avoid unnecessary exposure of individual comments or identities.
Specific data-handling, privacy and reporting requirements are agreed before work begins.
Start a conversation
Bring a sample, a question and the audience for the findings. We can discuss whether Discerne is a good fit.
"Delivery updates were unclear. I like the product, but I did not know when it would arrive."