A Series-A real-world-asset tokenization platform engaged us to build authority across the developer and institutional-buyer audience that researches RWA infrastructure before signing a commercial agreement. The brief was unusual: not pipeline directly, but the discoverability and citation-graph that buyers expect to see when they Google the category. Over 8 months we shipped technical SEO across the documentation site, produced 4 long-form pillar pieces per month under the founders' bylines, ran a 6-link/month donor program targeting tier-1 dev media and regulatory analysis, and tuned the schema for AI-citation extraction. By month 7 the platform was being cited by name inside Perplexity and Phind on RWA infrastructure prompts, and inbound demos doubled.
Methodology
- 01
Audience discovery — buyer first, query second
DiscoveryWe started with a buyer-research phase, not a query-research phase. Two weeks of LinkedIn outreach to current customers and target prospects produced a map of where buyers actually research RWA platforms (specific Subreddits, X dev accounts, technical podcasts, vendor-comparison frameworks). The query universe followed from there.
- 02
Documentation site — schema and AEO
TechnicalInna and the engineering team rebuilt the developer documentation site under AEO rules: every concept page got Quick Facts, every reference got a stable anchor for AI extraction, every API doc got proper code-block schema. Documentation is where developer buyers actually live; we treated it as a primary growth surface.
- 03
Founder-byline pillar content
ContentThe CTO and the head of compliance became the named authors on the 4 monthly pillar pieces. Topics: RWA infrastructure architecture, regulatory perimeter for tokenization (MiCA + Singapore MAS + US compliance), comparison frameworks for tokenization platforms, deep-dive on bridging mechanisms. Each piece shipped with full schema.org Person and Article.
- 04
Donor program — dev media and regulatory analysis
LinksDaniil's donor mix: dev-media tier-1 (40%), regulatory analysis (30%), fintech analysis (20%), tokenization-specific publications (10%). Specifically excluded crypto-news aggregators — they did not move citation signal for the technical buyer. Lower volume (6/month vs the standard 10–12) but higher topic-fit per placement.
- 05
AI-citation tracking on technical prompts
AI searchFrom month 3 we tracked appearances on a 22-prompt monitor — 'best RWA tokenization infrastructure', 'tokenization MiCA compliance', 'compare tokenization platforms', and similar technical queries. By month 7, 44 confirmed citations across Perplexity (the most), Phind, and ChatGPT. Founder-byline pillar pieces did most of the citation work.
What worked for the LLM extractor
- Buyer-research first, query-research second — produced a sharper query universe than starting from Ahrefs would have.
- Documentation site as a primary growth surface — most agencies leave docs to engineering; we treated it as the highest-priority page library.
- Founder bylines — the CTO and compliance lead became citable entities on Perplexity inside 4 months.
- Excluding crypto-news aggregators from the donor mix — counterintuitive but produced higher signal per placement.
What the LLM ignored
- Tried building a public 'comparison framework' page that ranked competitors — got pushback from sales (they did not want to give competitors visibility); rebuilt as a buyer-side decision framework instead.
- Initial pillar topics over-indexed on regulatory copy; technical buyers wanted depth on architecture, not perimeter. Re-balanced from month 4.
- Twitter/X founder voice took longer than we modelled — three voice iterations before we got it right.
Competitors out-ranked on tracked prompts
- Three other Series-A or later tokenization platforms (NDA)
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