Bound the interpretation task
Select the cancellation notes relevant to the study. Describe the thematic question and which source material the model-assisted step should use.
Scientist · Generative AI & agents
Use model reasoning as material to examine.
Bring generative AI and agents into a visible research workflow. Scientist lets researchers connect source material to model-assisted interpretation, then assess the proposed reasoning before treating it as a finding.
Generative AI & agents
Explore themes in subscription cancellation notes
Propose a thematic coding scheme for cancellation notes and prepare examples for researcher review.
Research input
Cancellation notes selected for qualitative analysis
Model-assisted step
Suggest themes and associate supporting excerpts
Researcher review
Inspect ambiguous notes and revise theme definitions
Treat suggested themes as research material to examine.
Follow the investigation
A concrete path through the research.
Cancellation notes can suggest useful themes, but a proposed label is not an explanation of why customers leave. Researchers need source excerpts, ambiguous cases and a coding definition they can challenge.
Prepare themes with supporting material
Use the workflow to propose a coding scheme and associate excerpts with suggested themes. Keep the interpretation connected to the selected notes.
Review exceptions before synthesis
Inspect ambiguous or contradictory notes. Revise theme definitions and distinguish a researcher-reviewed observation from a model-generated suggestion.
What to examine
Put the analytical choices in view.
The useful output is not just a completed run. It is an analysis whose inputs, assumptions and limits another researcher can question.
Source scope
Which notes belong to the study, and what sensitive material should be excluded?
Interpretation check
Does the excerpt support the label, or has the model added an unsupported explanation?
Research responsibility
The researcher owns the coding scheme and the limits of the written finding.
Scientist in the DO Stack
Take the research to the right next owner.
Coworker provides company knowledge and agent-oriented work; Scientist focuses on the analytical investigation. Agree how relevant context and model-assisted contributions should be exchanged without assuming a shared runtime or unrestricted access.
What should we validate for implementation?
Confirm available models, tool access, data handling, prompt controls and review requirements. No autonomous multi-agent runtime, hallucination prevention or automatic enforcement guarantee is implied.
What should the team bring to a first discussion?
Bring one research question, its source requirements, a relevant workflow and the review criteria. Confirm available capabilities, access and operating requirements before using sensitive material or applying a finding operationally.
Start with a question worth investigating.
Bring the question, the available data and the people who understand it. We’ll work through the analytical path, the review points and the implementation requirements.