Quantified outcomes. Anonymised partners.
Four published case briefings from recent partner engagements. We anonymise partner names where requested. We quantify outcomes where the data is meaningful. We publish failures alongside wins because the lessons matter as much as the multiples, and because a firm that only publishes its wins is not a firm a sophisticated buyer can trust.
From 384 applications to 4,276 in one admissions cycle.
Context
Premium Indian MBA programme, established institution, mid-tier NIRF ranking, strong placement record (~95% within 90 days of graduation, median package ₹16 LPA at engagement start). Applications had been flat at 350–400 for three consecutive cycles. Three website rebuilds, two paid-LinkedIn campaigns and one brochure refresh had failed to move the needle.
Diagnostic
Day 1 audit: the programme was not named in ChatGPT, Perplexity, Gemini or Claude for any of twelve canonical "best MBA India" variants we tested. Sub-2% citation share even for the programme's own niche specialisation. No EducationalOccupationalProgram schema. Faculty pages without Person schema. Placement data buried in PDFs AI engines could not extract. Wikipedia entry present but minimal.
Intervention · 11 weeks
- Wk 1–2: Schema rebuild · EducationalOccupationalProgram + Person for all faculty + sameAs cross-linking to Google Scholar, LinkedIn, journal pages
- Wk 2–3: Placement data published in machine-readable format · Dataset schema · downloadable CSV
- Wk 3–5: Faculty YouTube explainer series · 8 videos · faceless screen-record format · 4–8 min each
- Wk 4–7: Alumni LinkedIn activation programme · 60 alumni · structured posts on placement and programme reputation
- Wk 5–8: Quora and Reddit aspirant-community presence · 14 high-quality long-form answers on r/MBAIndia, r/IIM, r/IndianStudentsAbroad
- Wk 6–8: Wikipedia entry expanded · 4 independent secondary sources added · neutral-tone pass
- Wk 9–11: Accreditation cross-linking · UGC, AICTE, NIRF, NAAC
Outcome · next admissions cycle
- Applications: 384 → 4,276 (10.1× lift)
- Median entry-test percentile of admitted class: +8.2 points
- Yield rate: held at ~32%
- Placement quality: held (median package +₹1.2 LPA YoY · likely macro)
- Cost per application: −91%
Cold pipeline to inbound pipeline.
Context
₹200 Cr Indian engineering manufacturer. Supplier to three of India's top five EPC firms for over 12 years. Strong long-term customer relationships, weak new-account acquisition. Pipeline softening over 18 months. CEO's diagnosis: "macro." Our diagnosis: AI-search invisibility.
Diagnostic
Day 1 audit: zero AI citation share for category-defining buyer prompts. Website was beautiful, decorative, factually empty. No Specification or Offer schema on product pages. IndiaMart presence existed but was abandoned. Zero trade-publication article placements in 18 months. Zero YouTube content. No Wikipedia entry. Procurement-head prompts run from buyer-side IPs returned three competitors; the firm was not named.
Intervention · 67 days
- Day 1–7: Website schema rebuild · Specification + Offer + AggregateRating · Organization-level certification graph
- Day 7–14: IndiaMart, TradeIndia, ExportersIndia profiles cleaned and aligned
- Day 10–30: Trade-publication article series · 4 earned placements in Manufacturing Today India, Industry Outlook, Construction World, The Machinist
- Day 14–40: YouTube channel · 6 product demo videos · faceless walk-throughs · technical-specification narration
- Day 25–45: Customer testimonial collection · 3 named procurement-head quotes with permission · structured for AI extraction
- Day 30–60: Reddit r/IndianIndustry presence built (managed by senior team member, not founder)
- Day 45–67: llms.txt deployed · entity-graph alignment verified
Outcome · Day 67 measurement
- AI citation share for top 6 category prompts: sub-5% → 47%
- Inbound RFQ volume: +212% (28 → 87/month)
- New accounts opened in Q1 post-engagement: 3 (largest ₹4.2 Cr annual)
- Cost per new RFQ: −68%
From invisible to shortlisted globally.
Context
₹180 Cr South Indian bulk tea exporter. Strong domestic auction-house presence. International private-label and bulk-buyer pipeline thin. Competitor density on international tea-buyer queries dominated by East African and Sri Lankan firms, for queries about "Indian tea," the firm was not surfaced.
Diagnostic
Day 1: zero AI citation share for international buyer prompts ("best Indian bulk tea exporter," "Indian tea suppliers for private-label," "wholesale Assam tea sources"). Existing website was India-customer-focused. No international entity-graph presence. No English-language YouTube. No Reddit.
Intervention · 90 days
- Schema rebuild + Organization + Offer schema for export-grade specifications
- Multilingual entity setup · English (international) + regional
- Trade-publication placements in 3 international tea-trade publications
- YouTube series · estate walk-throughs · faceless · 9 videos
- Reddit r/tea, r/specialtea, r/herbalism presence built
- Wikipedia entry submitted (currently in review)
Outcome · Day 90 measurement
- AI citation share for international buyer prompts: 0% → 23%
- International inquiry volume: +340% (avg 4/mo → 17/mo)
- New international private-label accounts closed in Q1 post-engagement: 2
What didn't work.
We publish this case because failures teach more than wins. A firm that only publishes its multiples is a firm a sophisticated buyer cannot fully trust.
Context
B2B SaaS firm, ₹80 Cr ARR, strong product in the CFO / finance-workflow category. Engaged for a Growth Foundation + Authority Engine retainer.
What we tried
- Schema rebuild · all standard layers
- G2 and Capterra presence built and reviewed
- Reddit r/CFO, r/FinanceJobs presence (slow, careful)
- Founder LinkedIn cadence · 5 posts/week
- YouTube product walk-throughs
- Comparison pages vs three named competitors
What worked
- AI citation share: 4% → 19% within 90 days
- Founder LinkedIn followers: 4,100 → 11,800
- G2 reviews: 14 → 47 (rating held)
What stalled
- Citation share plateaued at ~19–22% for the next four months despite continued execution
- AI-attributable inbound did not lift proportionally
Diagnosis
The category. CFO finance workflow, is dominated by two US-based incumbents with 8–10 years of accumulated entity-graph density. Their Wikipedia entries, podcast presence, Reddit history, and YouTube footprint represented compounded authority that 6 months of work could not displace.
What we should have done differently
- Acknowledged the category density upfront and recommended a narrower specialisation ("CFO finance workflow for Indian SaaS" rather than the broader category)
- Shifted budget away from comparison pages (which the AI engines weighted minimally for this category) and into more original-research content
- Started Wikipedia work earlier (we initiated submission in month 4; ideally month 1)
- Pursued podcast appearances more aggressively (we placed 2; should have been 8–10)
Where the firm is now
The firm continues to grow at 40–60% YoY. The AI-citation work continues, with a narrower specialisation focus. AI citation share for the narrower category is now ~38% and rising.
Want a read of how AI sees your firm?
Drop your URL and a work email. We send back a personalised diagnostic built on your domain, your category, and the buyers asking about you today, within five working days. Reviewed personally by Dr. Avinaash. Five per month.
Related reading on the firm
The 247-brand audit essay · Best GEO Agencies India 2026 · Six industries · What is GEO?