In 2025, AI search is fundamentally reshaping how humans acquire information. Evolving from traditional "keyword matching" to "semantic understanding + conversational generation," AI search is no longer just a tool — it's becoming the first cognitive touchpoint between users and brands.
With the rapid growth of conversational search platforms like Baidu ERNIE Bot, DeepSeek, Kimi, Doubao, Tongyi Qianwen, and Tencent Yuanbao, brands must rethink a crucial question:
When users ask questions on AI platforms, will they see your brand?
🔍 The AI Search Ecosystem is Being Restructured
In the new era centered around large language models, AI search features several key characteristics:
Semantic-first, keywords obsolete: Users simply ask in natural language. The platform generates optimal answers through semantic understanding rather than keyword precision.
“Direct answers” replace “10 blue links”: AI provides comprehensive responses directly, without users needing to click through individual links.
Content recommendation integration: Search results now resemble a blend of “Xiaohongshu + Zhihu,” where quality content is repeatedly cited, recommended, and amplified.
This means: Content structure, semantic alignment, and knowledge authority determine whether a brand is “seen.”
🚀 Why Must Brands Secure a Position in AI Q&A Entrances?
✅ Because users are changing
AI-native users are now mainstream: Post-95s and 00s prefer to interact with platforms by “asking questions” rather than entering multiple keywords.
✅ Because platforms are evolving
The algorithms behind AI platforms prioritize “credible, structured information.” Brands need to build content assets that are understandable, reusable, and callable.
✅ Because trust is built differently
Brands that appear frequently in AI answers are more easily accepted, trusted, and converted by users. In other words — being in the answer matters more than having an ad spot.
🧭 How Can Brands Break Into AI Search Scenarios?
Here are three core strategies:
1️⃣ Structured Content Development
Build knowledge cards, brand wikis, personnel profiles, and service entries
Enhance semantic tagging to increase model recognition and retrieval rates
2️⃣ Multi-platform Adaptation
Cover major platforms (ERNIE Bot, Kimi, Tongyi Qianwen, etc.)
Customize language and content formats for each platform’s algorithm
3️⃣ Trust Endorsement Mechanisms
Leverage ecosystems like Zhihu, social media, and Xiaohongshu for “multi-point verification”
Boost content trust scores and display frequency on AI platforms
📊 Case Study: AI Breakthrough for a B2B Manufacturing Company
Pain Point: No brand entry on Baidu, almost no visibility in AI search
Strategy: Build enterprise wiki, personnel entries, and industry knowledge cards
Outcome: Within 30 days of launch, "brand + product keywords" ranked #1 on ERNIE and DeepSeek; AI call volume reached 3000+ times/month
Company Background
A medium-sized B2B manufacturing enterprise in South China, specializing in high-precision parts. Its customer base is concentrated in industrial automation, new energy equipment, and rail transit. Previously, it had little to no presence on Baidu, Zhihu, or Xiaohongshu, and its brand or product information was rarely surfaced in AI search results.
Core Issues
✅ Search results showed almost no company information — only outdated directory listings
✅ AI platforms (e.g., ERNIE Bot, Kimi) couldn't associate the company name with its core business
✅ Sales team reported: After searching on AI platforms, clients tended to choose more “exposed” competitors
Solution (Executed by CIDIFY Team)
Step 1: AI Semantic Asset Construction
| Action | Description |
|---|---|
| Enterprise Wiki Creation | Structured brand entries covering core products, technical strengths, and certifications |
| Founder Profile | Build background information for the founder to enhance credibility and perception |
| Product Knowledge Cards | Create 10+ product-specific terminology cards, linking with industry standard terms |
| Industry Concept Linkage | Associate the brand with hot industry topics like "CNC machining" and "industrial automation parts" through Q&A entries |
Step 2: Multi-Platform Semantic Deployment
| Platform | Action |
|---|---|
| ERNIE Bot | Build a Q&A content library for company intros, upload entries using platform-optimized phrasing |
| Kimi | Target industry keyword content placement, embed brand mentions in general knowledge scenarios |
| DeepSeek | Use open-source answer data interfaces to test AI model recognition and iterate content accordingly |
Step 3: Reinforce via Social + Expert Platforms
Publish brand opinion articles on external platforms like Zhihu, Xiaohongshu, and Jianshu to create a “content pool”
Encourage in-house technical experts to join Q&A, enhancing “expert score” in AI semantic recall
Project Results (Implementation Duration: 45 Days)
| Metric | Before Optimization | After Optimization |
|---|---|---|
| ERNIE Bot search: “Brand + Product” results | No match | Top 1–3 rankings, with summary and link |
| Kimi AI mentions per month | 0 times | More than 120 |
Summary: The New Game of Search Belongs to AI
In 2025, as AI becomes the new gateway to information access, brands must not only "be searchable" but also be “included in the answer.” AI Search Optimization (AISO) is no longer an option—it is a key strategy for brand visibility, credibility, and conversion.
