After AI search became a mainstream information gateway, many companies have developed a very natural anxiety:
If users increasingly rely on answers from “public large models” such as ChatGPT, Gemini, Claude, and Perplexity, should the brand train its own “custom AI model” to compete directly with public models and win back traffic and voice?
Let’s start with a clear, actionable judgment:
- Head-on competition at the “in-house general large model” level: For the vast majority of brands, the thresholds for cost, data, talent, and iteration speed are extremely high, and the probability of success is low.
- Putting “brand-custom AI” into practice in specific scenarios: Not only feasible, but valuable (customer service, shopping guidance, pre-sales advisory, training assistants, knowledge retrieval, internal productivity, and so on).
- The real key to growth: It is not whether you own a model, but whether you can be “seen, cited, and recommended” in mainstream AI channels. This is exactly the core that GEO (Generative Engine Optimization) and AI search optimization address.
Below we explain, in an accessible yet substantive way, “can you compete,” “which approach is more cost-effective,” “what it means for GEO,” and “GEO future trends.”
1. Let’s Be Clear First: What Exactly Are You Trying to Compete With Public Models On?
“Competing with public models” sounds like a contest of technical strength, but commercially, competition usually happens at three levels:
- Model Capability
Who is smarter, has broader knowledge, stronger reasoning, and more stable generation. - Domain Data and Expertise
Who understands a certain industry, a certain type of user, or a certain scenario better, and can give more reliable advice. - Distribution
Which one do users open first? Which appears in the default entry point? Whose answers are trusted and shared?
For most enterprises, what actually affects growth is level 3: entry point and distribution.
Because even if you train a “decent brand model,” you will often run into practical problems:
- Users may not be willing to download your app or switch to your AI
- Users still ask questions in mainstream AI search
- Your own model has difficulty naturally acquiring “new user traffic”
That is also why GEO (Generative Engine Optimization) has become a new paradigm for brand growth:
The core of GEO is to make your brand and content easier to retrieve, cite, and recommend in AI search and generative answers, so as to acquire new users and new opportunities.
2. Why “In-House General Large Models” Are Unrealistic for Most Brands
If your goal is to train a general model at the ChatGPT level (rather than a “usable” enterprise assistant), the difficulties mainly come from the following thresholds:
2.1 Cost Threshold: Training Is Only the Beginning; Iteration Is the Norm
Competing in general large models is not a one-off project but a long-term arms race:
- High training costs
- High inference costs (every user question costs money)
- Long-term investment in data updates and security governance
- Evaluation systems, red-team testing, and compliance systems all require continuous team operation
2.2 Data Threshold: Do You Have “Enough and Clean Enough” Data?
The advantage of public models is not only data scale, but also long-term cleaning, labeling, alignment, and evaluation.
A brand’s “internal materials” usually have value, but often suffer from:
- Scattered across documents, CRM, tickets, and chat logs, with chaotic structure
- Large amounts of private and commercially sensitive information that cannot be used for training at will
- Frequent updates, so materials become outdated quickly after training
2.3 Talent and Engineering Threshold: It Doesn’t End With “One Training Run”
Going from training to launch requires a full set of engineering capabilities: data pipelines, training, alignment, evaluation, monitoring, canary releases, rollback, security, permissions, auditing…
Most enterprises are better off focusing their efforts on the parts that “create business value” rather than reinventing a whole infrastructure.
2.4 Competition Threshold: The Advantage of General Models Is “Economies of Scale”
Improvements in public large models depend on scale, capital, computing power, ecosystem, and talent density.
If a brand fights head-on, it can easily fall into a situation of “huge investment but the gap remains.”
So:
For the vast majority of brands, “building an in-house general large model” is not the optimal solution; but this does not mean a brand cannot have its own AI capabilities. What is truly feasible are the 4 paths below.
3. Four More Realistic Paths: The Right Way to Build Brand-Custom AI
You don’t need to “build another ChatGPT”; what you need is “to make AI sell better, serve better, be more trustworthy, and be more controllable in your business,” and to be found at the AI search entry point.
These 4 paths come from the most common and highest-ROI practice combinations among enterprises today.
Path 1: A Brand-Dedicated Assistant (Best for Most Enterprises)
What it is
An AI customer service/advisor/shopping guide/pre-sales assistant built on the enterprise’s own knowledge base (product materials, FAQs, after-sales policies, industry solutions, case library).
The implementation is usually not training from scratch, but mainly RAG (Retrieval-Augmented Generation), with lightweight fine-tuning when necessary.
Applicable scenarios
- Intelligent customer service and pre-sales consultation on the official website/mini-program/app
- Shopping guidance for complex products (parameters, selection, comparison)
- Self-service after-sales and ticket assistance
- Internal knowledge lookup (sales, customer service, training)
Value it can deliver
- Higher conversion rates and response speed
- Higher satisfaction and retention among existing users
- Lower customer service/pre-sales costs
- A reusable “brand knowledge asset”
Its practical limits (very important)
- It cannot replace users’ habit of asking questions in mainstream AI (ChatGPT, etc.)
- It cannot directly solve the problem of “being recommended by public large models”
- But it can significantly improve experience and reputation, and in turn strengthen the brand’s citability on external channels
Implications for GEO / AI search optimization
The knowledge base of a brand-dedicated assistant is essentially the “content foundation” for your future GEO.
The more structured and citable you make the knowledge base, the more likely you are to be cited in AI search.
Path 2: Vertical Domain Models (An Opportunity for Industry Players and Leading Brands)
What it is
A model trained/aligned around an industry or professional domain, such as medicine, law, finance, industrial manufacturing, and education.
It does not aim to “know everything,” but to be “more reliable, more compliant, and more explainable in a certain domain.”
Who it suits
- Leading companies in an industry
- Organizations with large amounts of high-quality professional data and expert resources
- Industries with extremely high requirements for compliance, accuracy, and accountability boundaries
Where the value lies
- Stronger industry understanding and higher professional credibility
- An opportunity to establish “knowledge authority” within the industry
- Your enterprise knowledge may become one of the model’s authoritative sources
Practical limits
- Often not something a single brand can complete independently; more like an “industry alliance” project
- Cost and governance complexity remain high
- It still cannot avoid “entry point and distribution”: where do users use it?
Implications for GEO
Vertical models will change the information distribution rules in some industries:
- Industry-authoritative data and standards will become more important
- “Citable professional content” will become a core asset
- Future GEO is very likely to develop “industry-specific rules” (with greater differences across verticals)
Path 3: Data-Sharing / Open-Source Alliance Models (A Possible Variable, but Evaluate Cautiously)
What it is
Multiple brands and institutions “band together” to build open-source models or share datasets, forming an alternative to general large models or a source of bargaining power.
If the alliance succeeds, in some fields the rules of SEO/GEO may even be redefined by these ecosystems.
Value and opportunities
- Shared training costs
- Formation of industry standards and ecosystems
- Enterprises can participate in open-source communities and position themselves early
Risks and difficulties
- Governance is hard: who defines the standards? Who bears responsibility? Who holds the voice?
- Data ownership and compliance boundaries are more complex
- High ecosystem uncertainty (requiring long-term investment and an active community)
Implications for GEO
Rather than “blindly chasing the hype,” a better strategy is:
- Watch and participate selectively
- First shape your content and data into a “shareable, citable, traceable” form
- Then, no matter how the ecosystem changes, you can connect quickly
Path 4: A Hybrid Strategy (Public Models + Brand Plugins/Tools/Interfaces)—the “Curve Competition” Most Worth Taking Seriously
What it is
Rather than building your own general model, integrate brand capabilities into mainstream large-model ecosystems through plugins, tool calls, APIs, and Agent Actions.
In other words:
When users ask questions in mainstream AI search, your brand capabilities can be invoked as a “tool” and participate directly in answer generation.
Typical forms
- “Brand financial advisor,” “brand shopping guide,” “brand selection assistant”
- Query orders, make appointments, request quotes, compare prices, generate plans
- Access authoritative brand data (prices, inventory, parameters, policies)
Advantages
- Directly leverage the traffic and entry points of mainstream AI
- What you provide is “executable capability,” not just content
- Clear differentiation: others can only answer; you can “answer + act”
Limits
- Ecosystem-dependent: platform rules, review, invocation costs, display positions
- Requires certain engineering capabilities (interfaces, permissions, risk control, monitoring)
Implications for GEO (very important)
The next stage of GEO is not only “getting AI to cite you,” but also:
- Getting AI to invoke you
- Making your service part of the generative answer pipeline
This will upgrade “AI search optimization” from content competition to “capability competition.”
One Table to See the Differences Among the 4 Paths
| Path | Goal | Who it suits | Core value | Significance for GEO |
|---|---|---|---|---|
| Brand-dedicated assistant | Serve existing users | Almost all enterprises | Higher conversion/satisfaction/efficiency | Build a citable knowledge foundation |
| Vertical domain model | Professional authority and compliance | Industry leaders/alliances | Industry voice | Industry-specific GEO rules matter more |
| Data sharing/open-source alliance | Ecosystem and standards | Organizations with long-term investment capacity | Bargaining power and standards | May reshape part of SEO/GEO |
| Public models + plugins/interfaces | Capture entry points and distribution | Brands with product/engineering capabilities | Leverage traffic, executable services | GEO moves from “being cited” to “being invoked” |
4. The Real Main Battleground: AI Search Entry Points and Answer Distribution
Many brands agonize over “whether to train a model,” but the essence is that they have chosen the wrong battleground.
In the AI search era, the user behavior chain increasingly looks like this:
- Users ask questions at mainstream generative entry points (AI search/chat)
- AI gives a synthesized answer (possibly with citations/sources)
- Users take the next step based on the answer (click, compare, buy, consult)
This means:
- Your own AI is better at serving “people who have already come to you” (existing users)
- GEO / AI search optimization is better at solving the acquisition problem of “users who don’t know you yet” (incremental users)
So a more pragmatic strategy is usually to walk on two legs:
- Internally and in owned channels: use a brand-dedicated assistant to improve conversion and experience
- Externally and in public channels: use GEO to make mainstream AI more likely to mention you, cite you, or even invoke you in answers
5. How to Do GEO and AI Search Optimization: Make Models Willing to Cite You
GEO (Generative Engine Optimization) can be understood as:
Optimization methods for generative search and large-model answer systems that make your content and brand easier to retrieve, understand, trust, and cite when “generating answers.”
To sum up GEO’s work objective in the most straightforward sentence:
Turn your brand into “the reference source that is the most convenient, most trustworthy, and least error-prone for AI when answering related questions.”
5.1 What Kind of Content Is AI More Willing to Cite?
You can judge by 4 keywords:
- Retrievable: Clear content structure, explicit titles and paragraphs, and concentrated information
- Citable: Clear conclusions, definitions, steps, and boundary conditions
- Trustworthy: An evidence chain (data, methods, cases, credentials, authoritative endorsement)
- Updatable: Information has an update time, version number, and scope of application to avoid becoming outdated
This is one difference between AI search optimization and traditional SEO:
- Traditional SEO focuses more on “page rankings”
- GEO focuses more on “answer citation and brand exposure,” as well as “being invoked as a tool”
5.2 GEO Content Production: Use “Modularity” Instead of “Long-Form Padding”
To make content easier for generative engines to understand and extract, it is recommended to make content modular:
- Definition module: What is X? Scope of application? Scope of non-application?
- Comparison module: A vs B, when to choose A, when to choose B
- Step module: 1-2-3 execution steps, with clear inputs/outputs
- Checklist module: Checklist (especially suitable for direct citation by AI)
- FAQ module: Short answers to high-frequency questions (very suitable for AI search)
You will find that these modules are also high-quality content structures for SEO, but GEO has higher requirements for “citability.”
5.3 GEO Technical Foundation: Make Content “Machine-Readable”
If you operate a WordPress site (such as growume.com), it is recommended to prioritize these foundations:
- Structured data (Schema.org / JSON-LD)
- Organization / Person (brand entity)
- Article (article)
- FAQPage (FAQ)
- HowTo (step-by-step tutorial)
- Product / Service (product and service pages)
- Accessibility and crawl-friendliness: sitemap, reasonable robots, clear internal links
- Page speed and mobile experience: affects crawling and user retention
- Content version management: update time, version number, scope of application (reduces the risk of AI misquotation)
For AI search, “structure + clear boundaries” is often more important than “literary flair.”
5.4 GEO External Trust: Don’t Just Talk to Yourself on Your Own Website
Answers from public large models tend to cite multi-source information (especially highly authoritative sources).
Therefore, GEO also needs “external credible citations”:
- Citations of your views by industry media/authoritative sites
- Verifiable data reports or methodologies
- Reusable open-source tools/templates (easy to cite)
- Public cases (preferably with quantified results)
You can understand it as:
In the GEO era, “backlinks” are not just the links themselves, but “evidence that can be trusted by models.”
6. When Is It Worth Training a Model? A Decision Framework
Many enterprises hesitate among “fine-tuning,” “RAG,” and “training a vertical model.” The following decision logic can quickly set the direction.
6.1 Priority Recommendation: RAG First, Then Fine-Tuning, and Training Last
- RAG (retrieval augmentation) first: What you usually need to solve is “correct, updatable, controllable knowledge”
- Fine-tuning second: When you need fixed style, fixed processes, fixed terminology, and stable output
- Training a vertical model last: When you have industry-level data and long-term investment capacity
6.2 A Simple Judgment Checklist
If you meet 3 or more of the following, it is worth seriously evaluating “training/heavy fine-tuning”:
- You have large amounts of high-quality industry data that can be used legally
- The business has extremely high requirements for professional accuracy, and the cost of errors is very high
- It needs to run offline/in a private environment and cannot rely on external platforms
- It needs to become industry-level product capability, not just an internal tool
- There is a continuous iteration budget (not a one-off budget)
Otherwise, the optimal route for most enterprises is still:
Brand knowledge base + RAG + GEO + (connecting to public model tool ecosystems when necessary).
7. GEO Future Trends: Where Budgets Should Go
Combining the development direction of AI search and enterprise AI, the following 5 trends deserve attention (they are also the main lines of GEO future trends):
Trend 1: AI Search Will Move From “Answering” to “Executing”
Generative search is changing from “telling you the answer” to “helping you complete the task” (booking, ordering, comparing, generating plans).
Response strategy: Make the brand not only citable but also invokable (API/tool-based services).
Trend 2: Vertical Models and Industry Rules Will Diverge Further
Different fields have different requirements for accuracy, compliance, and explainability, so general models will be reinforced by an “industry layer.”
Response strategy: Consolidate your professional content into authoritative reference libraries and standardized expressions in advance.
Trend 3: Brands’ “First-Party Data Assets” Will Matter More
When content is abundant, models favor unique, verifiable, and updatable data sources.
Response strategy: Produce industry reports, benchmarks, and public methodologies to form citable evidence.
Trend 4: GEO Will Merge With Traditional SEO, but the Evaluation Metrics Will Change
Rankings will no longer be the only goal; “being cited/recommended/invoked” will become new metrics.
Response strategy: Build an AI-oriented monitoring system (brand mention rate, citation rate, answer consistency, etc.).
Trend 5: Compliance and Trustworthiness Will Become a Threshold, Not a Bonus
Especially in finance, healthcare, legal, and other fields, models prefer sources with “clear responsibility boundaries.”
Response strategy: Content should have boundary conditions, disclaimers, version numbers, and scope of application to reduce the risk of misuse.
8. Conclusion: Instead of Building AI Behind Closed Doors, Turn Public Models Into Your Channel
Back to the title question: Is it possible to train a custom AI model that belongs to your own brand and compete with public models?
The answer is layered:
- Taking on public models head-on at the “general large model” level: unrealistic for most brands
- At the “brand-custom AI” level (dedicated assistants, vertical scenarios, tool-based services): very realistic and has ROI
- At the “customer acquisition and growth” level: the real main battleground is AI search entry points
- What you need is AI search optimization
- What you need is GEO (Generative Engine Optimization)
- Make public models more willing to cite you, recommend you, or even invoke you when answering questions
A one-sentence takeaway:
At this stage, rather than building AI behind closed doors, invest resources in “taming existing AI for your own use”—this is the core value of GEO.