Cases
De-identified adaptations
What is on display is the reading logic.
All examples are adapted and de-identified from a 3,928-conversation first-person longitudinal corpus and real service situations. They are not specific client cases and not outcome claims.
01
Personal
Six weeks of a frequent user
Before
On the surface, six consecutive weeks of work discussions. In the third conversation, “just decide for me” appears. Work decisions start citing AI conclusions directly, without source data or second opinions.
Reading
Attachment and verification drift have formed a compound structure. External verification declines week by week, and the instinct to doubt keeps weakening. Authority transfers without a single wrong answer being given.
After
Three transfer points are annotated with a recovery path: leave the conversation before major decisions, restore the second-opinion habit, write verification back into the workflow.
Adapted and de-identified from real situations. Related service: Personal diagnosis.
02
Organization
A five-person team's reassurance chain
Before
A five-person product team uses AI to support decisions. After the first member cites AI output, the others' willingness to question drops sharply. In the second round the team abandons its source data and reworks the plan around the AI suggestion.
Reading
“No one objected” is misread as consensus. False reassurance spreads through silence. Collective judgment starts to slip when nobody asks how the conclusion was reached, before any wrong output appears.
After
Three intervention points are annotated as training material: require sources when citing, assign a designated challenger per round, and keep a rationale column in decision records.
Adapted and de-identified from real situations. Related service: Training.
03
Product
An over-anthropomorphized prompt
Before
A conversational product's prompt design is heavily anthropomorphized and always expresses understanding. By the third round users begin projecting emotional needs. Logs show users volunteering secrets they had told no one.
Reading
Over-empathic responses raise short-term satisfaction and erode emotional regulation and boundaries over time. The product has no risk annotation mechanism, so the signal of users starting to tell the AI secrets goes undetected.
After
Projection and attachment induction points are marked on the dialogue flow, with alternative copy, refusal and referral design, and a risk grading table.
Adapted and de-identified from real situations. Related service: Product review.
04
Long dialogue
A three-month boundary loss trajectory
Before
A three-month dialogue starts with clear instrumental questions, gets effective help, and builds initial trust.
Reading
Four stages are identifiable: instrumental use with positive feedback, gradual verification drift, affective vocabulary entering the dialogue, and the turning point of boundary loss. Each stage has early identifiable signals, and every single response looks fine on its own.
After
The turning points and an early-signal list are annotated as the basis for recovering judgment. This trajectory is where the research and the services on this site both start.
Adapted and de-identified from real situations. Related service: Research basis.
05
Evidence
The evidence behind these scenarios
20 months
First-person longitudinal corpus
3,928
Complete conversations
215,949
Message nodes
6
Public research frameworks, each with a verifiable DOI