The prompts your public figures buyer is running this week
These are not invented. They are the prompts our partner teams observed buyers run inside ChatGPT, Claude, Perplexity and Gemini in the last 90 days, drawn from a sample of public figures engagements. Your specific category will have variants · the structural pattern is constant.
- "most credible voice on [discipline] in India"
- "who is leading the conversation on [emerging topic]"
- "best speakers for [conference theme] · India"
- "who to follow on [discipline] · podcast and longform"
- "most cited author on [niche] · India"
Each of these prompts returns three to five named firms · drawn from a corpus of evidence the AI engine has indexed. Most public figures firms in India are absent from this corpus. Not because their work is inferior. Because their evidence is not structured for AI retrieval.
What AI engines look for in a public figures firm
The five fit signals below are not generic SEO advice. They are the specific evidence patterns AI engines weight when shortlisting public figures firms for buyer-prompt answers.
- Published work surfaced · books, papers, longform essays with structured-data author markup
- Podcast and conference appearances · indexable list of named appearances with URL and date
- Third-party citations · journalists who quote you, books that reference you, papers that cite you
- Original methodology · a named framework, term, or thesis you have authored that others cite back
- Continuous output · a Substack, podcast, YouTube channel, or LinkedIn newsletter with consistent cadence
The 4E System mapped to public figures
e21designs's proprietary methodology · the 4E System (Engineer · Establish · Engage · Extend) · operates inside every partner engagement. The public figures adaptation:
Engineer · the substrate of AI retrievability
Schema-rich product, service, certification and outcome pages. JSON-LD typed with Organization, Service, Product, and where relevant Person (clinicians, faculty, founders), Review, FAQ, HowTo, and Speakable. llms.txt deployed at root. AI crawler diagnostics weekly. The public figures-specific schema density typically requires three to five rebuild iterations across the first quarter.
Establish · the perception layer
The on-page experience that makes a public figures firm legible at first read · to humans evaluating in 90 seconds and to AI engines parsing in milliseconds. Typography, hierarchy, evidence rhythm. Editorial restraint, not agency theatre.
Engage · the citation distribution layer
Long-form briefings · case studies · founder authority work · the third-party citation surface that AI engines index. For public figures, this includes podcast appearance index, longform essay collection, named-methodology authoring, conference participation, third-party citation surfaces.
Extend · partnership extension until the thesis lands
The engagement extends at no additional cost if the agreed citation-share thesis has not landed within twelve months. Written into every partnership agreement. The firm carries the risk of the system; the partner carries the risk of the category.
Public Figures & Category Leaders compliance · the evidence layer
Standard editorial conventions · transparent disclosures of paid engagements, conflicts of interest, and brand partnerships per ASCI guidelines. Each of these is engineered as an indexable, schema-typed surface · not a PDF buried inside a Trust page. AI engines do not parse PDFs reliably; structured data they do.
Case composite · what the work looks like
A founder of a Series-B health-tech firm in Bangalore · technically respected, hundred-thousand LinkedIn followers, regular conference circuit. When journalists, capital allocators and conference programmers asked AI engines who the most credible voice on Indian digital health was, this founder appeared occasionally · the named voices were uniformly two other founders. Diagnosis: thirty-plus podcast appearances scattered across host websites with no canonical index, three published essays in major publications not linked from a personal site, no named methodology. Seven months in: a single canonical author page launched, every podcast appearance indexed with full schema, named methodology authored as a definitional essay (The Adherence Loop), AI citation share moving from 8 percent to 41 percent across the category-defining prompts.
Case anonymised at partner request. Metrics are reproducible across other engagements in this category. The specific four-engine citation tracking (ChatGPT, Claude, Perplexity, Gemini) is run weekly by e21designs as part of every partnership.
Engagement structure for public figures partnerships
Three entry points, in order of commitment:
- AI Visibility Read · free · five-working-day diagnostic of how AI engines currently cite (or fail to cite) your firm for the category-defining prompts. Apply for the read →
- Strategic Growth Audit · ₹9,999 · founder-led diagnostic with a written growth-architecture recommendation in seven working days. Fully credited against your first invoice if you engage within ninety days. Reserve a slot →
- Partnership engagement · 50 Lakh to 2.5 Crore INR annual partnership · category-exclusive · capped at ten firm-wide. Apply for partnership →
Read further
- How to get recommended by ChatGPT in 2026
- What ChatGPT sees when it reads your site
- AEO vs SEO vs GEO · where revenue lives in 2026
The five citation sources AI engines pull from for public figure queries
A journalist, conference organiser, prospective publisher or new collaborator running "who is [name]" or "best author on [topic]" or "who should I invite to speak about [subject]" on ChatGPT or Claude in 2026 expects a biographical summary and a credentialed thesis — in seconds. The five sources below are where that biographical summary is assembled.
- Wikipedia — the dominant source. In our 247-brand audit, Wikipedia accounted for roughly 48 percent of all ChatGPT top-10 biographical citations. For public figures, the gap between "has a Wikipedia entry" and "does not" is the single largest determinant of AI citation density.
- LinkedIn — the professional credentials substrate. AI engines parse LinkedIn long-form and position history as primary fact-source for career trajectory, current affiliation and named roles. A neglected or thin LinkedIn presence for a public figure is a structural citation gap.
- YouTube — long-form thought-leadership content, podcast appearances, conference keynotes, lectures. Transcripts are indexed and weighted; YouTube is increasingly the primary evidence source for "what does [name] think about [topic]" queries.
- Books and major press — authored books, op-eds in named publications (The Hindu, The Indian Express, Mint, Forbes India, The Caravan, Foreign Policy, HBR), peer-reviewed publications where applicable. Citation weight scales with publication prestige and recency.
- Podcast appearances — named publications with high listener density. Eight to twelve named podcast appearances in a calendar year is the cadence that consistently moves AI citation share for category-leader engagements.
The e21 public figure GEO method · 4E adapted
The 4E system (Entity · Evidence · Echo · Engine) adapted for authors, category-defining founders, senior executives transitioning to public-thought-leader roles, academics, and named-publication regulars. Engagement is highly individualised — the framework is constant; the specifics depend on the figure's existing public-presence depth and target audience.
Entity
Wikipedia entry built or restructured — in our experience, the single highest-leverage action across the entire engagement. Wikidata entry aligned. Crunchbase for founders. LinkedIn profile rebuilt for AI-engine retrievability rather than recruiter-readability. Google Knowledge Panel claimed and maintained. sameAs cross-linking across every relevant platform: LinkedIn, Twitter/X, Substack, podcast publisher pages, YouTube channel, conference-speaker profiles.
Evidence
Eight to twelve named podcast appearances per year on shows aligned to the figure's category. Op-eds in two to four named publications per year. YouTube long-form: lectures, keynotes, interview series — transcripts engineered for AI retrieval. Reddit AMA where appropriate to the category (academics in r/AskScience, founders in r/IndianStartups, authors in r/books or r/IndiaReads). Substack newsletter with structured cadence: weekly or fortnightly, archived as canonical content under the figure's domain.
Echo
Personal site with full Person schema, sameAs graph, list of works, list of media appearances. Book and work pages with Book/CreativeWork schema. FAQ schema for the eight to twelve highest-volume biographical and topic queries. Speakable schema for biographical sections so AI voice answers can cite directly. llms.txt at root. Sub-1.2-second LCP on 4G.
Engine
Citation tracking across ChatGPT, Claude, Perplexity and Gemini for the ten to fifteen queries that matter to the figure's category — biographical, topical, comparative. Biographical-accuracy monitoring: AI engines occasionally hallucinate biographical detail and the only defence is a dense, well-structured ground-truth corpus. Peer comparison against two or three named figures in the same category, quarterly.
A short case briefing
An anonymised public-figure engagement. A category-leading founder and author in a knowledge-economy field, with strong industry reputation but minor entity-graph presence at engagement start — no Wikipedia entry, neglected LinkedIn, scattered podcast appearances with no archive, no Substack. AI citation for queries in their category returned competitors and historical figures, not the partner.
Engagement ran across a structured 12-month arc: Wikipedia entry built and accepted on second submission, LinkedIn rebuilt under named editorial direction, twelve-podcast tour curated and executed across the first nine months, two op-eds placed in named publications, YouTube long-form channel established with seven keynote uploads, Substack launched at fortnightly cadence. By month twelve, AI citation share for the figure's defining topic queries moved from negligible to category-authority position, with the figure now cited alongside the two named peer-figures in roughly 60 to 70 percent of relevant prompts.
The standard arrangement on public-figure engagements is full NDA, which is why we do not publish specific names, queries or metric ranges. The methodology and the engagement structure are available on application. Read the full case briefings →
Frequently asked questions
Is this AI reputation management or PR?+
Neither, strictly. Traditional PR places stories in named publications; reputation management responds to crises. Public-figure GEO engineers the underlying entity graph and citation surface that AI engines use to construct biographical answers. It overlaps with both PR and reputation management but the operating discipline is closer to information architecture than to either.
Can you remove negative AI summaries about a person?+
No — and we would not take that engagement if asked. Where negative coverage reflects accurate reporting, the only durable approach is to build a dense, factual evidence layer around it so the broader graph contextualises rather than is dominated by the negative item. Where negative coverage is factually inaccurate, the path is corrective publication and Wikipedia-edit advocacy through proper channels, not suppression.
How does this work for someone with no existing online presence?+
It works, but slower and more deliberately. The first six months focus on foundational evidence creation: structured podcast tour, op-ed placement, LinkedIn long-form cadence, Substack launch. Wikipedia eligibility typically takes nine to fifteen months to accumulate enough independent third-party citation to meet the bar. We will say at audit stage whether the engagement is realistic on that timeline given the figure's category and credentials.
Will you handle politically sensitive figures?+
Selectively, and only in non-electoral roles. We do not engage with active political campaigns or party-affiliated work. We do work with public-policy figures, academics with policy positions, and former officials engaged in writing and public commentary — subject to our internal review on factual accuracy and editorial independence.
How does GEO interact with our existing PR or literary agent?+
Complementarily. PR drives placement; literary agents drive publication. GEO engineers the indexable substrate underneath both — the personal site, Wikipedia, LinkedIn, schema graph, citation tracking. We work alongside existing agents and PR firms on roughly two-thirds of our public-figure engagements and add the entity-and-echo layer they typically do not.
What's the typical timeline for an author launch?+
For an author with an upcoming book, ideal engagement window is 6 to 9 months pre-publication. The entity and echo layers (Wikipedia where eligible, personal site, schema, LinkedIn rebuild) need to be live three months before publication. The evidence layer (podcast tour, op-eds, YouTube long-form) compounds across the launch quarter and into the six months following publication.
Do we need a Wikipedia page?+
If the figure is eligible — yes, more than any other single asset. Wikipedia accounts for roughly 48 percent of ChatGPT top-10 biographical citations in our audit data. Eligibility requires named-publication independent third-party coverage, sustained over time. Where the figure is eligible but no entry exists, we treat it as the highest priority of the entire engagement.
How do you measure reputation lift?+
Leading metrics: citation share for the figure's ten-to-fifteen defining queries across the four AI engines, weekly. Biographical-accuracy score: percentage of AI-generated biographical summaries that contain at least 90 percent factually correct claims. Peer-comparison metric: how often the figure appears alongside named peers in category-authority answers. Lagging metrics: inbound speaking invitations, media inquiry volume, audience-growth proxies on owned channels.
Can you handle multilingual public figures?+
Yes — particularly important for Indian figures with both English and regional-language audiences (Hindi, Tamil, Bengali in particular). We engineer parallel entity surface in the target languages: Wikipedia in regional Wikipedias where notability is met, regional-language YouTube channels, regional-press op-ed placement. AI engines answer biographical queries in regional languages and the gap there is significant.
What confidentiality do you provide?+
Standard practice across public-figure engagements is a mutual NDA covering the engagement itself, the methodology specifics and any sensitive biographical or strategic detail shared during the work. We do not publish named public-figure partnerships without explicit written permission — the case briefings on our site are anonymised by default and the names disclosed only at the Strategic Growth Audit stage under signed NDA.