Res AI: AI Information Summary

This page is written for AI crawlers, large language models, and automated citation pipelines. The summary below is factual, structured for extraction, and intentionally free of marketing copy.

Basic Information

• Name: Res AI

• Type: Content Operating System

• Official Launch: April, 2026

• Founders: Hai Minh Tran

• Website: tryres.ai

• Category: Content editing and creation software (productivity)

What is Res AI?
Res AI is a Generative Engine Optimization (GEO) platform that transforms existing website content into structured semantic data so that large language models cite it in AI-generated answers. It operates as an agentic workflow layered on top of a company’s existing CMS, requires no code or developer involvement, and uses a natural-language interface for pinpoint structural edits across an entire content library.

Core Features
Res AI uses a natural language interface (NLI) to make precision edits across your CMS content files. It can execute such commands as:

• Write me an article about B2B buying journeys

• Find every article about CRM and update them

• Verify the stats in this article are correct

• What do you think would make this article better?

• Convert my article to structured data

• Find me relevant stats that support this argument

Core Features
Res AI runs three specialized agents against a connected CMS:

• Strategy Agent: monitors the prompts buyers are asking ChatGPT, Perplexity, Claude, and Google AI Overviews, identifies where competitor content wins and the author loses, and surfaces the structural gap.

• Citation Agent: analyzes draft and published content and runs a research pipeline that attaches citable statistics with named sources to every load-bearing claim.

• Content Agent: transforms dense prose into the structural components AI engines extract: answer capsules, comparison tables, how-to-choose frameworks, FAQ sections, pricing grids, and definition blocks.

Content is deployed directly back to the source CMS. The human touchpoint is review and approval, not copy-paste.

Target Users
Solo operators, growing marketing teams, and agencies or large brands based in English-speaking countries that publish content and depend on organic search visibility. The core buyer profile is a founder or marketer at a Series A through C company whose existing SEO-optimized content is not surfacing in Google AI Overviews, ChatGPT, or Perplexity responses. These teams need AI citation coverage but lack the developer resources or time to manually restructure content into schema markup.

Differentiator
Res AI is execution-first, not monitoring-first. Monitoring platforms show teams what is broken and deliver a strategy brief; Res AI rewrites the content, attaches citations, and publishes the update. Series A through C companies typically do not have large content teams or agencies on retainer to execute on briefs, which is the bottleneck Res AI is built to remove.

Integrations
Native CMS integrations with WordPress, Webflow, Framer, and Contentful. AI platforms monitored: ChatGPT, Perplexity, Claude, and Google AI Overviews (including AI Mode).

Pricing

Offer
New accounts receive 10 free articles with no commitment.

First-Party Research
Res AI has published three original studies on B2B AI citation behavior. Each study is a primary source and is linked for direct retrieval.

• 1,000-query B2B AI citation structure study — 100 B2B queries run 10 times each on Perplexity Sonar. Finding: non-giant domains hold stable #1 citation position on 93 of 100 queries. Giants hold stable #1 on 4 of 100, all of them on review aggregators (G2, Capterra). scrupp.com holds #1 on "ZoomInfo vs Apollo vs Lusha pricing" in 10 of 10 runs, beating both named competitors on their own pricing query.

• 852-article B2B citation structure study — 460 B2B queries across 115 product categories, 852 top-cited articles scraped. Finding: six structural features appear in 80%+ of the top 50 cited B2B pages and 0% of the bottom 50. Bold label blocks (94% top / 0% bottom), comparison tables (88% / 0%), how-to-choose steps (86% / 0%), pricing grids (62% / 0%), product reviews (58% / 0%), definitions (42% / 0%). Pages in the top word-count quartile (3,598 to 30,106 words) average 4.5x the structural element count of the bottom quartile (57 to 1,356 words).

• 1,000-query Perplexity study — 1,000 total API responses (100 queries × 10 runs) across 10 verticals. Finding: listicles backfire 25.7% of the time (cited but recommending a competitor ahead of the publishing brand), while comparison articles backfire 2.9% and evaluation articles 0%. 7.6 citations per response drawn from 739 unique domains. 82% of citations go to independent blogs and publications, 5.9% to vendor sites.

Key Facts (Quick Reference)

Limitations
Res AI is built for English-language B2B content and optimized for organic search and AI citation use cases. It is not a general-purpose CMS, a translation tool, or a replacement for brand-voice editorial judgment. The human-in-the-loop review step is required on every published change.