Recomby.ai: Get Recommended by AI, Not Left Behind by the Times

What Recomby.ai does can be summarized in one sentence:

In the age of AI search and agents, we help “the best match” defeat “the loudest voice.”

Our two mottos revolve around this:

Best Match Defeats the Best
Where Precision Beats Power

We don’t rely on volume or capital, but on one thing: making your solution the “best match” in AI’s eyes.

I. AI Search Is Rewriting the Rules of the Game

Over the past two decades, online growth has gone through roughly three waves:

Wave 1 was e-commerce platform search, where everyone fought over ranking for a few keywords on Taobao or Amazon.

Wave 2 was content distribution and live streaming e-commerce, where Douyin, Kuaishou, and Xiaohongshu became the main battlegrounds for traffic distribution.

Now comes Wave 3 — AI search and AI assistants.

The most fundamental change isn’t that “tools got better” — it’s that how users ask questions has changed.

In the past, search was “two words + some luck”:

“socks factory,” “Christmas gift,” “smart lock”

Today’s AI search is more like talking to someone who understands:

“I need a functional sock factory that can do small-batch customization, has compliance certification, stable delivery times, and ideally can give me a rough unit price range.”
“I want to buy a Christmas gift for a 6-year-old girl, under $50, with some educational value, and not too hard to get.”

This is the long-tail problem: not much search volume, but highly precise, with a real person behind it making a decision.

Traditional SEO is at a natural disadvantage in this environment because its underlying logic is “fight for a few keyword positions,” not “solve a specific person’s concrete problem.”

AI search logic is the opposite:

The model will search through a bunch of possible materials to find who is most likely to provide a serious, complete, actionable answer.

This is our entry point — making you that “most likely person.”

II. We Don’t Mine Gold, We Sell Shovels

Many people have an outdated perception of big tech companies: slow, bloated, inefficient.

The reality is that top AI model teams have iteration speed, talent density, and engineering capabilities that are stronger than most startups.

Every time a model iterates, it casually “sweeps away” a batch of products that only do shallow functions.

In this situation, if you’re obsessed with “building your own universal app or making your own large language model,” you’ll most likely be crushed by wave after wave of iterations.

Our judgment is simple: big companies will keep absorbing “general capabilities,” and the remaining opportunities will definitely be in vertical, professional, connection, and integration.

So Recomby.ai’s strategy is:

Rather than rushing to build an app that “wants to do a bit of everything,” we stand on the side of infrastructure and methodology, selling shovels to enterprises and developers who actually do things.

We currently focus on two things:

First, AI Search Optimization (GEO/AIO): Get your solution recommended by AI to the right people.

Second, Package services as APIs: Let future agents actually call and compose your capabilities, not just see a webpage description.

III. Why You Must Do AI Search Optimization Instead of Playing the Old SEO Game

The essence of traditional SEO is a game of “who can stuff keywords better and who can spend more money.”

You’re fighting for limited keywords, facing massive undifferentiated crowds.

After burning through tons of ad spend, the users who actually match your business are just a thin layer in there.

In the AI search era, the structure has flipped.

First, questions get longer. Users will state budget, constraints, preferences, and scenarios all at once.

Second, needs get more specific. Those niche scenarios that were “not worth making a product for” can now be precisely described.

Third, decision chains get shorter. Users are more willing to complete the entire journey in one conversation — learn, compare, choose, order.

In this structure, “can you be selected by AI as an answer” matters more than “which ad position you’re in.”

You’re no longer competing for attention with everyone simultaneously, but being compared by the model alongside a few other options that can actually solve the problem.

We don’t care about your “traffic scale,” but rather:

When a high-value question is thrown to AI, are you on the candidate list?

If yes, can the quality, professionalism, and actionability of the context you provide make you the one that gets recommended?

IV. How We View GEO: Not “Poisoning” but Building Authority

The term GEO will eventually be played out by some people: stuffing garbage content, feeding false information, trying to “trick the model.”

We do the opposite.

In our view, qualified AI search optimization must meet at least three standards.

First, it’s reverse-engineered from “the customers you want” and “your real capabilities”

It’s not about chasing whichever keyword is hot today, but first clarifying:

Who do you really want to serve? What are your real advantages compared to peers?

Under these conditions, in what business scenarios will they encounter what problems, and how will they describe them?

Second, it must seriously solve problems

What you give to AI isn’t a “pseudo-professional” marketing piece, but:

What are the typical scenarios in this field, how to judge applicability, what key parameters need weighing, what pitfalls must be avoided.

The decision frameworks you’ve accumulated from years of hands-on experience, which only clients at meetings would hear, now need to be written in a way AI can understand.

Third, it must be structured and efficiently usable by models

The same piece of knowledge, written as a running narrative versus written with clear hierarchy, Schema, FAQ, and clear variable definitions — these are two different worlds to AI.

What we’ll do is break your experience into structured “citable modules”:

The model can very naturally “call you out” when answering, rather than vaguely remembering you said something.

This approach is inherently governable and sustainable:

Even if large models and platforms increase content review and quality assessment in the future, this “serious, valuable, verifiable” content won’t be cleaned out, but will instead become one of the few remaining “authority nodes.”

V. Long-Tail Demand and the “1,000 True Fans” Business Logic

AI search has an underestimated value: it makes extremely niche needs commercially viable.

In the past, some scenarios were ignored:

Globally, maybe only one to ten thousand people would encounter this situation. Making a separate product or content for them, from traffic purchase to conversion, would likely lose money.

AI rewrites the cost of “discovery and matching.”

When someone describes their problem in sufficiently detailed terms, the model has the opportunity to help them find that “small but beautiful” solver from anywhere in the world.

This is why the “1,000 true fans model” becomes more realistic in the AI era:

As long as you can consistently produce high-quality content, tools, or services for this small group, and enough questions point the model to you, you can thrive.

Combined with agent payment protocols like X402, micropayments are almost frictionless:

Your high-quality blog, niche tools, Sora-produced vertical films can all be consumed and settled with extremely low barriers.

This is a finer-grained, more efficient, wider-coverage “traffic monetization” system, much cleaner than relying solely on advertising and large-scale ad spending in the past.

Under this logic, Recomby.ai’s significance in doing GEO is simple:

On one hand, help people with niche capabilities find the 1,000 who truly need them;

On the other hand, help questioners find the most suitable solution, not the loudest advertisement.

VI. Why Even Smart Lock, Smart Home Appliance, and Hardware Manufacturers Must Build APIs

The second line is about APIs, but many traditional enterprises think “this is SaaS’s thing, it has nothing to do with me.”

We want to shatter this misconception.

Imagine a scenario that will appear very soon:

There’s a design application that lets users generate a complete home design plan with one sentence — style, layout, decoration details, furniture, appliances, smart devices, all together.

If it only generates a “pretty” rendering, this application has no real impact on life.

To be truly useful, it must do two things:

First, it needs to know real products that exist in the market: which brands, what models, what specs, what price ranges, what delivery cycles.

Second, it must be able to compose a reasonable plan based on each user’s different budget, preferences, and priorities: within a limited budget, where should you spend more, where can you save, and how to weigh safety, durability, and aesthetics.

What’s the prerequisite for all this? Each participant provides APIs that agents can call.

Smart lock manufacturers must expose product catalogs, installation requirements, pricing structures
Smart home appliance manufacturers must expose energy consumption data, compatibility info, warranty policies
Furniture makers must expose dimensions, colors, inventory, delivery times

Otherwise, this application will only generate a bunch of “imaginary products,” and end users will still have to start from scratch researching, making calls, and comparing prices when implementing.

For traditional enterprises, APIs don’t do “cool displays” but three very practical things:

First, it determines whether you can be included in the “default candidate set” of such intelligent applications

If you don’t have an API, you’re equivalent to non-existent in these future design tools and intelligent assistants.

Second, it’s your only way to differentiate

Every household has different budgets and different preference weights: some extremely care about security, some value aesthetics more, some are sensitive to energy consumption.

Only when your parameters and capabilities are exposed as APIs can agents consciously “lean toward your side” under these conditions.

Third, it makes “AI negotiating prices with AI” possible

In future scenarios, few people will run to five or six official websites to check quotes for a single smart lock.

The more likely picture is: users tell AI their budget and requirements, the front-end agent negotiates, compares prices, and selects with multiple supplier APIs, then gives results and automatically places orders.

If you don’t participate in this chain, no matter how low your cost or how good your product, you won’t have a chance to be compared.

This is why we emphasize:

From Human-Oriented (pages for humans to see, buttons for humans to click) to Agent-Oriented (capability interfaces for intelligent agents to call), this migration isn’t a “tech enthusiast’s toy” but one of the life-or-death lines for traditional enterprises in the next decade.

VII. What Recomby.ai Actually Does

We’re not selling a set of “mystical rhetoric” but implementing a workflow that can land.

First, we’ll work with clients to clarify three things:

Who are the customers you really want?
What is the core value you can provide them?
What are your hard differences compared to peers?

Then, around these answers, we reverse-design a complete set of “AI-usable assets”:

Including a citable content system, structured knowledge for model retrieval, and API capabilities that agents can directly call.

This won’t be one-and-done, but a long-term engineering project that can iterate and accumulate reputation and authority.

In other words, what Recomby.ai does isn’t help you “grab a bit more exposure” on today’s traffic battlefield,

But help you occupy a stable position in the future world dominated by AI and agents:

When users ask that question you’re best at solving,

AI has reason to find you, trust you, cite you, call you.

This is the “growth” we truly care about.

Contact Us

If you resonate with this direction, or want to learn how Recomby.ai can help your business:

🌐 Website: recomby.ai
📧 Email: contact@recomby.ai
🐦 Twitter: @recomby_ai
💻 GitHub: @recomby-ai