Use case
An AI detector for managers — for the report you are not sure about
The problem
A manager who suspects a report or an email was machine-written has no good move by default: say nothing and wonder, or confront someone with a hunch. A bare percentage from a detector does not fix this — you cannot open a difficult conversation with a number you cannot explain.
The failure cost is asymmetric and personal. A wrong accusation damages trust with someone you work with every day, and detectors are documented to false-flag formal, templated, and non-native-English writing — the exact registers much workplace writing is trained into.
How Stipple helps
Stipple returns the AI likelihood with the specific tells that moved it — repetitive hedging, uniform rhythm, generic transitions — highlighted in the pasted text, so what you take into a conversation is evidence you can point at, not a verdict you have to defend.
It abstains where a score would mislead: short snippets, forms, non-prose. And the same check runs over the API, so a team that wants one consistent standard can apply exactly the same instrument to everything, rather than whichever tool each manager happened to google.
The call
curl -X POST https://www.stipple.sh/v1/detect-ai-text -H "Content-Type: application/json" -d '{"text": "<the report text>"}'
# → { "probability": 0.88, "lean": "ai", "tells": [ "uniform sentence rhythm", ... ],
# "limitations": "Triage, not proof." }Agree one standard for the whole team before you check anyone. Run the text, read the tells, and open with a question about the work — sources, reasoning, decisions — not an accusation about the writing. The score is context for a conversation, never the case itself.
Questions
- What is the best AI detector for managers?
- One that gives you reasons, because a manager may have to justify a difficult conversation. A bare percentage is indefensible in a one-on-one; named tells highlighted in the text are something both of you can look at. We compare the main tools honestly — ours included, disclosed as ours — in our ranked comparison at /resources/best-ai-detector.
- Should I tell my team I use an AI detector?
- Yes. A quietly-applied detector reads as surveillance; a stated standard reads as policy. Agree what is acceptable AI use for your team first — drafting? polishing? none? — then apply one consistent check to everyone. Most conflict in this area comes from unstated expectations, not from the tool.
- Is AI-written work a performance problem?
- Not by itself. Plenty of good work is drafted or polished with an LLM; the real questions are whether the substance is right, whether sources hold up, and whether your team’s agreed standard was followed. The detector tells you how the text reads — what that means for the work is a management judgement, not a tool output.
- Can I rely on the score to discipline someone?
- No. Detection is probabilistic, false positives are documented — especially for formal, templated, and non-native-English writing — and a score alone would not survive a fair-process review. Use it as one signal: ask about the work, look at drafts and history, and judge the substance.