Built for every kind of website
Whether you run an e-commerce store, a SaaS product, a blog or a news site — Wrenda gives AI search engines what they need to recommend you.
E-Commerce
The problem
Product pages are JavaScript-heavy and content-thin. AI crawlers see empty shells — no specs, no schema, no context.
How Wrenda solves it
Wrenda pre-renders product pages and enriches them with Product schema, specs, comparisons, and buying guides.
- Higher product page citations in ChatGPT shopping queries
- Schema-rich product data for Google Shopping AI
- Pre-rendered pages fully indexed by traditional bots
SaaS & Software
The problem
Feature pages and docs aren't semantically rich enough for AI to understand your capabilities or recommend you over competitors.
How Wrenda solves it
Wrenda adds capability definitions, use case expansions, and comparison context AI needs to recommend your tool.
- Get recommended in "best tool for X" AI queries
- Feature pages enriched with entity-rich semantic context
- Competitive positioning content added automatically
Publisher & Blog
The problem
Articles compete with thousands of similar pieces. AI systems cite authoritative, structured content — generic HTML doesn't qualify.
How Wrenda solves it
Wrenda enriches articles with entity definitions, author authority signals, and structured FAQ sections.
- Articles cited in AI-generated summaries and answers
- Author credibility signals added automatically
- Topic authority through entity expansion and linking
News & Media
The problem
Breaking news needs to be understood by AI in real time — context, named entities, and verified facts matter for citations.
How Wrenda solves it
Wrenda adds structured news schema, entity context, and real-time enrichment so AI cites your coverage first.
- News articles cited in AI news digests and roundups
- Named entity recognition and contextual linking
- Real-time schema enrichment without server changes
After adding Wrenda, our traffic from AI sources grew 4x in 90 days. ChatGPT started recommending our products in shopping queries we never expected. The schema enrichment alone made a visible difference in the first week.
Marcus Teller
Head of Growth, Novaloom Commerce
Which use case fits you?
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