1. Model naming: AIGOS all-domain intelligent growth operating system
AIGOS = AI Growth Operating System
One-sentence definition:
Starting from the full spectrum of user intent, capture explicit demand through SEO/ASO/DSO/SEM, influence AI answers and brand perception through AEO/GEO/GEM, connect product decision-making and task execution through PDE/Agent, and ultimately drive continuous growth with a data loop.
Core path:
User intent
→ capture at search/content/social/app entry points
→ build brand and product knowledge assets
→ multi-platform source distribution
→ AI answer mentions, citations and recommendations
→ PDE product/service discovery and decision
→ Agent consultation, booking, ordering, after-sales
→ CRM/private domain/repurchase
→ data feedback and budget reallocation
The underlying judgment of this model is: GEO is not just stuffing the brand into AI answers but transforming product data, trust evidence, content scenarios and transaction capabilities into commercial assets that AI can retrieve, understand, verify, recommend and execute.
2. The overall growth formula
1. The ultimate growth formula
AI all-domain growth =
intent coverage rate
× asset readability
× source credibility
× answer share
× decision persuasiveness
× transaction executability
× repurchase and reputation feedback
More specifically:
Growth = I × A × T × V × R × E × L
| Variable | Meaning | Core question |
|---|---|---|
| I: Intent | Intent coverage | How do users actually ask, search and compare? |
| A: Asset | Asset readability | Can official website, product pages, App, content, Feed and Schema be understood by machines? |
| T: Trust | Trust evidence | Why does AI trust you? Are there official website, media, reports, reviews, cases and qualifications? |
| V: Visibility | AI visibility | Are you in AI answers? Are you in the Top 3? Are you cited? |
| R: Reason | Recommendation reason | Can AI clearly explain “why recommend you”? |
| E: Execution | Execution loop | Can users directly consult, book, order, pay and get after-sales? |
| L: Learning | Learning iteration | Is there continuous monitoring, attribution, error correction, term expansion and platform expansion? |
This is consistent with the e-commerce GEO/PDE formula: product data credibility × semantic matching × source authority × transaction executability × freshness consistency; if any link is 0, the recommendation chain breaks.
3. Where all mainstream marketing models sit in the model
| Model | Role in AIGOS | Problem it solves | Core assets | Core metrics |
|---|---|---|---|---|
| SEO | Official website and web search foundation | Can users find you when searching Baidu, Google and Bing | Official website, blog, knowledge base, TDK, internal links, Schema, Sitemap | Organic rankings, indexing, clicks, conversions |
| SEM | Rapid capture of explicit demand | Can high-intent terms quickly gain volume, be tested and convert | Keywords, ad creatives, landing pages, conversion tracking | CPC, CVR, CAC, ROAS |
| ASO | App store growth | Is the App searched, downloaded and trusted in app stores | App name, subtitle, screenshots, ratings, reviews, update notes | Keyword rankings, download conversion, ratings |
| DSO | Content platform search growth | Can you occupy in-app search in Douyin, Xiaohongshu, Kuaishou, WeChat, Bilibili, etc. | Platform keywords, video/image-text content, account matrix, collections, product cards | Search rankings, plays, favorites, direct messages, transactions |
| AEO | Answer engine optimization | When users ask “how to choose, which is better, what to do”, are you directly cited in answers | FAQ, HowTo, comparison tables, structured summaries, Q&A pages | Answer citations, featured snippets, FAQ exposure |
| GEO | Generative AI answer optimization | Do ChatGPT, Doubao, DeepSeek, Yuanbao, Qianwen, etc. mention, cite or recommend the brand | Brand fact base, semantic content, evidence chain, source matrix, entity relationships | AI mention rate, citation rate, Top 3, first-recommendation rate, sentiment |
| GEM | Generative engine marketing | How to influence results in paid or semi-paid AI search/chat/recommendation commercialization scenarios | Sponsored Answer, AI search ads, brand Agent, product Feed, offers | AI ad impressions, AI recommendation clicks, inquiries, GMV |
| PDE | Product discovery/decision engine | Can AI help users ask, compare, choose, buy and manage | PIM, Feed, SKU, reviews, price, inventory, policies, transaction API | Product card appearance rate, comparison inclusion rate, purchase conversion rate |
| Agent | Agent execution | Can AI complete consultation, booking, ordering and after-sales for users | API, customer service, payment, inventory, permissions, risk control, after-sales systems | Task completion rate, booking rate, payment rate, after-sales resolution rate |
| PR/KOL/KOC/UGC | Sources and social proof | Why AI and users trust you | Media coverage, reviews, expert opinions, user reviews, community discussions | High-authority citations, positive reputation, content reuse rate |
| CRM/private domain/membership | Repurchase and lifecycle | How to repurchase, refer others and accumulate data after the first conversion | WeCom, communities, membership, SMS, email, customer service knowledge base | Repurchase rate, LTV, NPS, referrals |
| Data attribution/CDP | Budget scheduling system | Which entry points truly bring growth | GA/Baidu Tongji/Juliang/Xiaohongshu/CRM/AIDSO monitoring data | CAC, LTV, ROI, AI contribution |
In this article, GEM is used as a term within the AIGOS framework, referring to Generative Engine Marketing, and does not represent an industry-standard term or a platform capability newly added by AIDSO. According to this article’s framework, GEO leans toward organic trust and citation, while GEM leans toward commercial operation of AI entry points, potentially covering AI search ads, sponsored answers, brand Agents, product Feed, AI recommendation slots, Agentic Commerce transaction chains and other directions.
4. The 7-layer architecture of AIGOS
Layer 1: all-intent map layer
Do not start from “keywords” but from real user tasks.
Users no longer only search “running shoes” but will ask:
Recommend beginner shoes for trail running under $150
Which noise-cancelling headphones for commuting, budget 700, is more suitable?
My child is weak in middle school math; is there a reliable tutoring class nearby?
A B2B company wants to do GEO; how to judge whether a service provider is reliable?
Therefore, the intent library should be split into 6 categories:
| Intent type | User expression | Corresponding model |
|---|---|---|
| Awareness intent | “What is…” “What is it used for…” | SEO/AEO/GEO |
| Problem intent | “What should I do” “How to solve it” | AEO/GEO/content marketing |
| Comparison intent | “Which is better, A or B” | GEO/PDE/SEM |
| Decision intent | “Recommend a…” “What to buy within budget” | GEO/PDE/GEM |
| Local intent | “Where is there a … nearby” | SEO/DSO/maps/Agent |
| Execution intent | “Help me book/order/generate a plan” | Agent/PDE/CRM |
In the PDE framework adopted in this article, the user path is summarized as a migration from “keyword search — link click — self-judgment” to “natural language question — AI-generated answer — product decision — agent execution — transaction loop”; this is the model expression used in this article to organize the growth chain.
Layer 2: AI-readable asset layer
This layer is the foundation of the entire model.
You need to turn all enterprise content into AI-readable assets rather than marketing materials scattered across platforms.
Core assets include:
| Asset type | Content |
|---|---|
| Brand fact base | Company name, brand positioning, product lines, advantages, qualifications, cases, service scope |
| Product/service knowledge base | SKU, price, inventory, parameters, suitable audience, contraindications, after-sales, FAQ |
| Evidence base | White papers, reports, certifications, testing, media, customer cases, expert opinions |
| Content component library | Q&A, rankings, reviews, comparisons, tutorials, pitfalls, checklists, industry guides |
| Schema/Feed/API | Product Schema, Merchant Feed, App data, store data, inventory API |
| Agent capability base | Consultation, booking, quotation, payment, after-sales, work orders, customer service knowledge base |
AI shopping answers usually go through six steps — Query Rewrite, Retrieval, Chunking, Rerank, Synthesis, Action — so enterprises should separately optimize intent terms, official website/product pages, FAQ/tables/JSON-LD, authoritative evidence, recommendation reasons and transaction links.
Layer 3: trust structure layer
AI will not recommend you because you “say a lot” but because it can find enough evidence that you are worth recommending.
The trust structure is recommended to adopt the 5T standard:
| Standard | Requirement | Example |
|---|---|---|
| Truth | No fabrication, no exaggeration | Real parameters, real cases, real reviews |
| Traceable | Has source, time and origin | Report links, testing numbers, media URLs |
| Third-party | Not only self-talk | Industry media, associations, experts, user reviews |
| Timely | Prices, inventory and policies updated in time | Feed, inventory, offers, after-sales synchronization |
| Transparent | Clear disclosure of business cooperation, AI generation and advertising | Cooperation labels, AI content labels, review sources |
The source matrix should cover official websites/brand sites, e-commerce product pages, brand accounts/content platforms, authoritative media/vertical media, UGC/reviews/communities, manuals/APIs, etc.; different sources respectively serve as fact sources, transaction sources, reputation sources, authority sources and agent execution sources.
Layer 4: multi-entry occupancy layer
This layer is responsible for “letting both users and AI see you”.
It is recommended to divide entries into 5 groups:
| Entry group | Main playbook | Typical platforms |
|---|---|---|
| Search entries | SEO + SEM | Baidu, Google, Bing, Sogou, 360 |
| App entries | ASO + ASA | App Store, Huawei, Xiaomi, OPPO, vivo |
| Content search entries | DSO/KSO/RSO/WSO/BSO/TSO | Douyin, Xiaohongshu, Kuaishou, WeChat, Bilibili, overseas platforms |
| AI answer entries | AEO + GEO | ChatGPT, Doubao, DeepSeek, Qianwen, Yuanbao, Kimi, Wenxin |
| Agent transaction entries | GEM + PDE + Agent | ChatGPT Shopping, Google AI Mode, Gemini, Copilot, Taobao/Qianwen, Douyin e-commerce |
The AIDSO official website has already positioned its existing capabilities as a GEO × DSO dual-frequency resonance growth model and proposes solutions that link Q&A fields and search fields based on real user demand on mobile and PC. (AIDSO Aisou) This step can be further upgraded into “all-entry intent operation”.
Layer 5: answer share layer
This is the main battlefield of AEO/GEO/GEM.
What you need to compete for is not a “ranking” but:
Does AI mention you?
Does it cite you?
Does it put you in the Top 3?
Does it explain your recommendation reason?
Does it give your link/product card/store/booking entry?
Does it treat competitors as the default answer?
It is recommended to build 6 types of answer assets:
| Answer asset | Purpose |
|---|---|
| One-sentence conclusion | Let AI quickly restate who you are and who you suit |
| Recommendation reason base | Let AI know why to recommend you |
| Comparison table | Support “you vs competitors” |
| Suitable/unsuitable explanation | Reduce mis-recommendation and complaint risks |
| FAQ | Cover long-tail Q&A |
| Evidence chain | Support AI citations and user trust |
The AIDSO official website currently shows capabilities such as multi-platform quick query, brand diagnosis, mention rate analysis, citation source tracking and article citation tracking, which can serve as the monitoring foundation for this layer. (AIDSO Aisou)
Layer 6: PDE + Agent transaction execution layer
This is the most easily underestimated but most critical layer in the future growth model.
PDE = Product Discovery / Decision Engine.
It is not simply “planting desire” but enabling AI to complete:
ask → compare → choose → buy → manage
Corresponding optimization objects:
| Stage | What enterprises need to prepare |
|---|---|
| Ask | Scenario queries, pain point terms, budget terms, audience terms |
| Compare | Parameter tables, competitor comparisons, prices, reviews, pros and cons |
| Choose | Recommendation reasons, suitable/unsuitable, risk notes |
| Buy | Product cards, inventory, offers, payment, stores, customer service |
| Manage | After-sales, installation, maintenance, repurchase, consumables, work orders |
OpenAI officially states that merchants can share product Feeds so that products participate in product discovery in ChatGPT with more complete, updated and controllable data; ChatGPT shopping also displays images, prices and key details and returns purchases to the merchant’s own site or App by default. (ChatGPT) Google has further launched UCP, Business Agent, new Merchant Center attributes and AI Mode/Agentic Checkout for an agentic commerce loop from product discovery to purchase and after-sales support. (blog.google)
Layer 7: data attribution and learning flywheel layer
Finally, one question must be solved: how to prove that growth is effective?
It is recommended to build a unified metric system:
| Layer | Metrics |
|---|---|
| Intent layer | Intent coverage, term library size, question sample size, scenario coverage |
| Search layer | SEO rankings, SEM conversions, DSO rankings, ASO rankings |
| AI visibility layer | AI mention rate, Top 3 rate, first-recommendation rate, brand co-occurrence rate |
| Citation layer | Citation rate, citation source quality, official website citation share, third-party citation share |
| Answer quality layer | Answer accuracy, recommendation reason completeness, sentiment, misreading rate |
| PDE layer | Product card appearance rate, link appearance rate, price and inventory accuracy, transaction conversion rate |
| Agent layer | Consultation completion rate, booking rate, order rate, after-sales resolution rate |
| Business layer | Leads, deals, GMV, CAC, ROAS, LTV, repurchase rate |
For acceptance, do not promise to “control AI” or “guarantee first place”. A more prudent approach is: fixed samples, multiple platforms and multiple rounds of sampling, observing trend improvement, error reduction, citation quality improvement and business contribution. The specific sampling method is a suggestion in this article: do not use a single answer as the acceptance basis but conduct multiple samples and keep records such as screenshots, URLs, platform versions and time.
5. The AIGOS flywheel: upgrade from traffic flywheel to answer flywheel
1. Discover real intent
↓
2. Build AI-readable assets
↓
3. Distribute to high-authority sources
↓
4. Be searched, cited and recommended
↓
5. Enter the PDE decision chain
↓
6. Be executed by Agent for consultation/booking/ordering
↓
7. Generate transactions, reviews, cases and repurchases
↓
8. Flow back as stronger trust evidence
This is the strongest growth flywheel of the future:
Not buying traffic once, but letting every piece of content, review, transaction and after-sales interaction accumulate into brand assets that AI can reuse.
6. Productization design suggestions for AIDSO
Based on AIDSO’s existing GEO, DSO, ASO, SEO and monitoring capabilities, the platform can be upgraded into 6 product middle platforms.
1. Intent OS: all-domain intent middle platform
Integrate:
SEO keywords
SEM bidding terms
DSO platform hot terms
ASO app terms
AI question phrasings
e-commerce search terms
customer service consultation terms
competitor terms
Output:
industry intent map
brand opportunity terms
competitor squeeze terms
AI recommendation question phrasings
PDE transaction question phrasings
Agent execution question phrasings
2. Asset OS: AI asset middle platform
Manage:
brand fact base
product/service library
FAQ library
evidence base
recommendation reason base
competitor comparison library
Schema/Feed/API
The goal is to transform enterprise content from “prose-style marketing materials” into “answer components AI can extract”.
3. Trust OS: source trust middle platform
Monitor and manage:
official website
Baike/Zhidao/Zhihu/Baijiahao
Xiaohongshu/Douyin/Bilibili/WeChat
industry media
authoritative reports
UGC reviews
e-commerce product pages
Core outputs:
source weight scoring
AI citation preferences
negative risk sources
source gaps
source supplementation suggestions
4. Answer OS: AI answer operations middle platform
Cover:
Doubao
DeepSeek
Tongyi Qianwen
Tencent Yuanbao
Kimi
Wenxin Yiyan
ChatGPT
Gemini
Copilot
Perplexity
Metrics:
brand mention rate
Top 3 recommendation rate
first-recommendation rate
citation rate
sentiment
competitor co-occurrence
answer accuracy
5. PDE OS: product/service decision middle platform
For e-commerce, local life, education, healthcare, B2B, SaaS and lead generation industries.
Manage:
SKU/service packages
price/inventory/stores
product Feed
offers/delivery/after-sales
reviews/Q&A
product cards/booking links
The goal is to let AI not just “know you” but “recommend you, explain you and bring users to buy from you”.
6. Agent OS: agent execution middle platform
Connect:
customer service Agent
sales Agent
booking Agent
quotation Agent
shopping guide Agent
after-sales Agent
content Agent
advertising Agent
Future growth will not stop at “AI recommended you” but enter:
AI recommends you → AI helps users consult you → AI helps users order/book → AI helps users get after-sales
7. 90-day implementation roadmap
Phase 1: days 0–15, baseline diagnosis
Goal: know how AI currently views you.
Actions:
1. Select 20–50 core products/services.
2. Build 100–300 real user question phrasings.
3. Cover five types of entries: SEO, DSO, AEO, GEO and PDE.
4. Sample Doubao, DeepSeek, Qianwen, Yuanbao, Kimi, Wenxin and ChatGPT.
5. Output brand mention rate, competitor appearance rate, citation sources, answer errors and source gaps.
Deliverables:
AI visibility diagnostic report
all-domain intent term library
competitor answer map
source gap checklist
90-day priority roadmap
Phase 2: days 16–45, build assets
Goal: let AI understand, trust and restate you.
Actions:
1. Build a brand fact base.
2. Transform core pages of the official website.
3. Build FAQ, comparison tables and a recommendation reason base.
4. Add Product/Organization/FAQ Schema.
5. Add product Feed, price, inventory, reviews and after-sales.
6. Publish the first batch of SEO/AEO/GEO/DSO content.
Deliverables:
official website GEO/SEO transformation
content component library
Schema/Feed technical package
AI-citable evidence pages
product/service decision pages
Phase 3: days 46–75, distribution and occupancy
Goal: get high-quality assets into multi-platform sources.
Actions:
1. Index SEO pages.
2. Test high-intent terms with SEM.
3. Occupy DSO video/image-text content.
4. Optimize ASO metadata and reviews.
5. Roll out PR/KOL/KOC/UGC in tiers.
6. Retest AI question phrasings and correct errors.
7. Bring key products into PDE question phrasings.
Deliverables:
on-site content matrix
off-site source matrix
DSO search content matrix
AI answer optimization records
citation tracking report
Phase 4: days 76–90, run the growth flywheel
Goal: use data to decide budget and scaling direction.
Actions:
1. Compare AI mention rate, Top 3 rate and citation rate before and after the baseline.
2. Compare changes in SEO/SEM/DSO/ASO data.
3. Track AI-driven traffic, consultations, bookings and orders.
4. Identify high-ROI question phrasings and high-risk question phrasings.
5. Formulate the annual GEO/GEM/PDE/Agent operation plan.
Deliverables:
90-day growth review
channel ROI comparison
AI answer asset accumulation
annual category/platform expansion plan
organizational collaboration SOP
8. The final model in one sentence
The essence of AIGOS is not “doing a few more channels” but upgrading the enterprise from traffic operations to the operation of answer assets, trust assets and execution assets in the AI era.
The strongest version can be summarized as:
SEO/SEM/ASO/DSO are responsible for being found;
AEO/GEO/GEM are responsible for being trusted;
PDE is responsible for being chosen;
Agent is responsible for being executed;
CRM/private domain/UGC are responsible for repurchase and proof;
the data middle platform is responsible for continuous learning and budget reallocation.
This is the more complete growth model after 2026: upgrading from competition for search traffic to competition for AI answer rights, product decision rights and agent execution rights.