Yes. The system prompt can ingest target school tiers from a WordPress page (M7, T15, T25, regional) and recommend a section emphasis accordingly. An M7-bound applicant gets a 705+ floor framing; a regional T30 applicant gets a 645 floor framing. Update the tier page and the bot reflects the new framing on the next conversation, no retraining needed.

If your WordPress pages reference GMAT Focus (Quant, Verbal, Data Insights sections, no AWA, no IR as a separate section), the bot reads and uses that framing. The system prompt should explicitly note 'GMAT Focus only, not legacy GMAT' so the bot doesn't drift into Sentence Correction or AWA topics that no longer apply to current applicants.

It can describe your ESR review process and queue an ESR PDF submission for your tutor to analyse before the trial. The bot itself doesn't analyse the ESR in chat: that requires a tutor reading the percentile rankings, section timing breakdown, and question-type accuracy. Most services use the bot to capture the ESR and route it to the right Quant or Data Insights tutor before the consult.

If your hour-bundle pricing is on the pricing page, yes. The bot surfaces trial-session discounts in context and explains when the 25-hour package is cheaper than singles plus separate mock fees. It won't invent discounts; if you want a specific code honoured, add the rule to the prompt. Otherwise it quotes only what's published.

Yes. The 28+ display conditions let you run a GMAT bot on /gmat/ pages, a GRE bot on /gre/ pages, and an admissions consulting bot on the consulting pages, each with their own tone and tutor roster. Conversation logs stay separate so you can review GMAT R2 enquiries without scrolling past admissions consulting threads.

Logs stay in your WordPress database. Bring-your-own-key sends prompts directly to OpenAI, Anthropic, or Google with no third-party processor in between. That matters because MBA applicants share sensitive details: target schools, prior application history, employer, undergraduate GPA. You control retention and redaction, and the plugin doesn't add a new vendor between you and the model.

The system prompt should forbid promising specific score jumps (no 'we guarantee +60'). The bot describes typical practices: 50-80 hours for a 60-point Focus gain, weekly missed-item review, 5-6 full-length proctored mocks. It quotes realistic floors and ceilings for the weeks remaining and routes to the consult for any guarantee terms or atypical cases.

The bot handles reapplications explicitly. It collects the prior cycle's outcome, the score on file, and the new target tier, then recommends a Data Insights or Quant retake plan tuned to the gap. For applicants who scored well on legacy GMAT and need to retake on Focus, the bot flags the format change up front and routes to your Focus-specific transition plan.