Pre-scan case memos, board briefings, executive summaries, capstones, and recruiting essays before Turnitin or your professor sees them. Sentence-level highlights show which lines read AI, with perplexity and burstiness signals so you can fix the prose instead of guessing. Built around the recommendation-led memo format that programs like HBS, Wharton, Booth, Kellogg, Stanford GSB, Sloan, Stern, INSEAD, and LBS reward. Free to try. No card.
MBA writing is a narrow set of genres written under tight deadlines and graded against a recommendation-led standard. The same compact format that earns marks in a case memo is the format ChatGPT defaults to, which makes pre-scanning essential for first-year cohorts before professors learn your real voice.
The MBA stack runs from first-term case write-ups through second-year integrative capstones, with group projects and recruiting essays running alongside both. Pre-scanning fits every layer because the institutional report at the end is the same Turnitin AI check, regardless of whether the deliverable is a 600-word memo or a 30-page capstone section.
Three to ten cases a week during core terms at HBS, Booth, and Kellogg. Free tier covers a single 5,000-character paste, which is enough for one full memo. Pro at $19.99 a month, or $14.99 on yearly, unlocks 10,000-character pastes and unlimited scans for the weeks where you are submitting cases across operations, finance, and strategy in parallel.
Heavier synthesis, denser exhibit references, and professors who already know what AI-shaped prose looks like. The sentence-level highlights matter here because integrative memos reward specific recommendations and a single AI-rewritten paragraph can be the one your professor questions in a cold call. The 90-day Pro history is the safety net.
Multi-section deliverables that get scanned section by section as they come together. The 10,000-character cap forces you to scan in sections, which matches how faculty advisors actually read drafts. PDF export keeps a defensible record of which version of each section was scanned and when, useful when a sponsor company asks about a draft you sent three weeks ago.
Generic detectors treat every submission the same. Case memos, board memos, executive summaries, recommendation memos, and financial analysis writing each have their own structural risks because the genre conventions themselves overlap with ChatGPT defaults.
Recommendation up front, three to four supporting paragraphs, exhibit references, action plan close. The compact recommendation-led structure is identical to the structure ChatGPT produces by default when asked to write a case memo. Cases written entirely by hand can still read AI-shaped because the genre rewards exactly that compression. Pre-scanning catches the overlap before your section professor does.
One page, audience is a non-expert executive, structure is decision, recommendation, tradeoffs. Briefings reward clean topic-sentence-first paragraphs and uniform sentence length, both of which are AI-default patterns. Burstiness is the metric to watch here, because a tight briefing with low burstiness reads AI even when every word is yours.
Standalone summaries on top of a longer deliverable, three to five paragraphs, no jargon, no exhibits. Executive summaries are the highest false-positive risk genre on the whole MBA stack because the writing conventions and ChatGPT defaults converge completely. Scan every executive summary before submission, treat anything below 70 as a rewrite candidate.
Used in strategy, marketing, and operations courses. Argument-driven, transitions matter, evidence chains have to be visible. The transition phrases that earn marks ("Given this, however, on balance") are also the transition phrases ChatGPT defaults to. Edit those phrases in your own voice before submission.
The hybrid genre: numbers from your model, prose interpreting them. Detectors read the prose, not the model. The risk is that interpretation paragraphs ("revenue growth of 8 percent suggests sustained customer expansion") read identical to ChatGPT defaults because the analytical phrasing is highly constrained. Vary phrasing across paragraphs to keep burstiness up.
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First-year case-method cohorts at HBS, Booth, and Kellogg submit three to five memos a week. Pre-scanning has to fit inside that cadence or it never gets used. Here is the workflow that survives core terms.
Read the case, build your own model in Excel, draft the memo in Word or Docs from your own analysis. Using ChatGPT to brainstorm the recommendation or to debug a model is the realistic 2026 default. Writing the prose itself in ChatGPT is the failure mode that detection catches.
Open app.textsight.ai thirty minutes before the section deadline. Paste the memo. Scan. Free tier handles 5,000 characters in one paste, which covers a standard case memo. Pro handles 10,000, which covers extended write-ups and integrative submissions. The scan returns in about thirty seconds with an Authenticity Score and a sentence-by-sentence colour map.
Above 75, submit. Between 50 and 75, look at the red sentences and rewrite those specifically. For case memos the highest-risk paragraph is usually the recommendation block at the top, because the genre rewards exactly the compact phrasing ChatGPT defaults to. Edit that block in your own voice from your own model.
One round of editing usually moves a borderline score by 15 to 25 points. Re-scan, confirm you are in the safe band, then submit through Canvas, Blackboard, or your school portal. A typical case memo round-trips in about six minutes; a capstone section in about fifteen.
Top consulting and banking recruiters have increasingly moved to screen application essays for AI content. A flagged recruiting essay can quietly drop your application out of the pipeline, so a pre-submission self-check is now sensible practice.
Admissions offices at top programs increasingly run essays through Turnitin or equivalent AI checks during read. A submission that reads as high-AI is hard to recover from at interview. This genre is harder than academic work because admissions essays reward exactly the polished introspective voice ChatGPT defaults to. Pre-scan every draft, rewrite the flagged sections in your own register, then submit.
Consulting firms have increasingly added AI checks to the personal-statement and case-experience essays in their applications. The genre is short and structured, which means it sits in the high-overlap zone with ChatGPT defaults. The pre-scan workflow before you submit is the same as for academic memos: paste, scan, edit the red sentences, re-scan, submit.
Bulge-bracket banks have increasingly added AI-content review to recruiting applications. Banking essays are shorter and more formulaic than consulting essays, which makes them higher risk for false positives on detection. Vary phrasing across the "why finance" and "why this firm" sections to keep burstiness up.
Coaching essays through ChatGPT and submitting the output verbatim is the failure mode detection catches. Using ChatGPT to brainstorm experiences, draft outlines, or debug awkward phrasing is widespread, defensible, and rarely surfaces as a detection flag if the final prose is written by you from your own notes. The pre-scan is the gate that tells you which side of that line you are on.
MBA group projects produce some of the messiest writing on campus. Four-author memos, six-section capstones, mid-merge drafts where one team-mate used ChatGPT and three did not. Generic detectors produce a flat percentage. Sentence-level detection is what teams actually need.
A group memo where two sections read AI-shaped and three sections read human will return as something like 55 percent on a generic detector. That percentage tells the team nothing useful. The TextSight sentence-level map shows exactly which two sections are the problem and which contributor needs to rewrite. That granularity is what the team actually needs in the final hour before submission.
Each contributor drafts their own section. The team lead pastes the merged draft into TextSight, scans, and circulates the sentence-level map. The two contributors with red flags rewrite their sections. Re-scan the merged draft, confirm the score is above 75, then submit. This adds about ten minutes to the group hand-off and saves the team from a single high-AI section sinking the whole deliverable.
For high-stakes deliverables (capstone final, field consulting client deck, integrative practicum), each contributor scans their own section before the merge. That way the team lead is merging sections that already cleared the score floor, and the final merged scan is a confirmation rather than a triage exercise.
The Business tier ($29.99/mo yearly) includes 5 team seats and shared history. For capstone teams running across a full term, this means every section scan and every revision is visible to the whole team, with PDF export for the final hand-off package. Most MBA capstone teams that adopt TextSight settle into this tier by the second half of the term.
Programs like HBS, Wharton, Booth, Kellogg, Stanford GSB, Sloan, Stern, INSEAD, and LBS widely use Turnitin or equivalent AI checks. Where that pre-Turnitin culture exists, a pre-submission self-check is the difference between editing a flagged sentence yourself and explaining it to a committee later.
Honor code framing, not auto-fail policy. Undisclosed AI submission is treated as an honor code breach with sanctions up to and including dismissal, but the institutions stop short of single-percentage cut-offs. Sentence-level evidence, a student conversation, and review of earlier drafts are the standard process. Pre-scanning before submission is now widespread among second-year cohorts, who learned the cost of skipping it during their first year.
Similar honor-code framing with explicit AI policy language in the student handbook. Sloan in particular publishes detailed guidance on disclosed AI use that is acceptable (research, brainstorming, debugging) versus undisclosed use that is not (drafting prose, generating analysis). The TextSight scan plus PDF report is the format a Sloan integrity committee actually wants to see.
US east-coast and mid-Atlantic programs that have rolled out formal AI-detection policies on coursework. Field consulting deliverables and capstones tend to draw the most scrutiny, because the deliverable goes to a sponsor company and AI residue can sour the engagement. Pre-scanning those sections first is the sensible priority.
European and Indian programs with high non-native English candidate populations. False positives on international candidates are a known issue across AI detectors, where carefully-constructed Indian English and continental European English can over-flag. This is the case for reading the sentence-level highlights rather than reacting to a single percentage: a structured memo from a non-native writer often flags on conventions that reflect how you were taught, not actual AI use.
A single percentage is not a fix path. The TextSight result panel shows which sentences reacted and why, with paragraph-level rollups for longer memos and capstone sections, so you can edit the specific lines instead of rewriting the whole submission.
Every sentence is colour-coded by its own AI-likeness score. Red sentences clustered in your recommendation block are a stronger signal than scattered yellows. Scattered yellows in otherwise structured memo prose often just mean you were taught to write the case-method way. You read the pattern, not just the headline number.
Each paragraph gets its own card with score, dominant signals, and the worst-offender sentence. Useful when a memo is structurally fine overall but one paragraph (usually the recommendation or the executive summary) drifts AI-shaped. The card view points to that paragraph directly instead of making you scan the highlight map by eye.
Perplexity is how predictable your word choices are to a language model. Low perplexity reads AI-like. The score is shown per-sentence on Pro, which is the diagnostic context you need to decide whether a flag is real AI residue or just standard business-school phrasing that a recommendation memo rewards.
Burstiness is how much your sentence length and structure vary across the section. ChatGPT defaults to uniform medium-length sentences. Case memos and executive summaries reward exactly that uniformity, which is why burstiness is the metric MBA candidates need to watch most closely. Vary sentence length on purpose to keep burstiness up without breaking the genre conventions.
More for MBA candidates.
The graduate-school page covering master's coursework and dissertation chapters.
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See pricing →Free to try. No card. Built around case memos, briefings, and capstones for candidates at programs like HBS, Wharton, Booth, Kellogg, Stanford GSB, Sloan, Stern, INSEAD, and LBS.
How TextSight fits other teams and workflows.