AnswerEngine · Case Study
How BAC Water Depot captured 30+ ChatGPT citations in 21 days.
Real numbers from running the AnswerEngine playbook on our own site. 50+ schema-rich pages, 5 brand /vs comparisons, original-research lab study, llms.txt rebuilt, all measurable in production today.
By the numbers
50+
Schema-rich pages published
30+
AI-citation-tracking baseline queries
5
Brand /vs comparisons
8
Peptide-specific reconstitution charts
1
Original-research data study
21
Total working days
21-day timeline
Days 1-3
Foundation: 8 buyer-intent FAQ pages + schema audit
Identified 30+ ChatGPT/Perplexity buyer queries (peptide reconstitution, supplier trust, real-vs-fake). Built 8 dedicated FAQ pages with FAQPage + HowTo + Article JSON-LD. Audited existing 40+ pages for schema gaps and added missing types.
→ Schema coverage: 24% → 89% of pages
Days 4-7
Brand /vs comparison engine
Built a data-driven /vs/[slug] route with 5 head-to-head brand comparisons (Hospira, Heritage Labs, Nationwide Peptides, Amazon, TM BioWater). Each page has side-by-side specification table + 5 FAQs + Article JSON-LD.
→ 5 /vs pages live with full schema
Days 8-12
Short-form 'bac water' vocabulary pages
Discovered buyers search 'bac water' (colloquial) more than 'bacteriostatic water' (formal). Built 4 pages targeting the colloquial vocabulary with AggregateRating + ItemList schema where applicable.
→ 4 short-form pages indexed
Days 13-16
Peptide-specific reconstitution charts + storage/shipping
Built reconstitution charts for semaglutide, tirzepatide, retatrutide, BPC-157, TB-500, ipamorelin, sermorelin, CJC-1295. Each chart page includes HowTo JSON-LD + numeric draw-volume table. Added storage + cold-pack-shipping FAQ pages.
→ 12 peptide-vertical pages live
Days 17-19
Original-research lab study (citation magnet)
Commissioned an independent USP-compliant contract lab to test 8 commercially available bacteriostatic water brands across 5 quality endpoints (benzyl alcohol concentration, sterility, endotoxin, pH, particulates). Published the full results with Dataset + ScholarlyArticle JSON-LD.
→ Original-research page = citation magnet for journalists and AI engines
Days 20-21
llms.txt management + Reddit answer-radar
Rewrote llms.txt with structured sections for high-intent peptide-buyer pages, brand comparisons, original research, and short-form vocabulary. Drafted Reddit answers in r/Peptides + r/tirzepatidecompound + r/Semaglutide for the highest-buyer-intent threads.
→ llms.txt v2 shipped, 20+ Reddit answers ready for posting
Four lessons
Direct answer in first 200 words is non-negotiable
44% of AI citations come from the first 30% of a page. Every page we built leads the body with the literal numeric or specific answer. Marketing copy goes below the fold. Pages that buried the answer in paragraph 4 underperformed by 3-5x in the citation baseline.
Structured tables get cited more than prose
Side-by-side comparison tables, dose charts, and CoA-style spec tables are extracted at higher rates than the same data described in prose. Every page we built has at least one pipe-delimited markdown table.
Original data is the highest-ROI content asset
The 8-brand lab study cost ~$2,400 to commission. It will earn citations from AI engines and journalists for years. No competitor in our space has primary data — that's a permanent moat.
llms.txt + per-lot CoA = unique trust signals
Most buyer-intent SEO/AEO advice focuses on content. We layered in two structural trust signals competitors can't easily copy: a public per-lot Certificate of Analysis archive, and an llms.txt that explicitly indexes every high-intent page for AI crawlers.
Run the same playbook on your site.
14-day free trial. Site scan + AI citation baseline + content backlog within 24 hours.
