Imagine that we don't want to deal with all the big data analysis. That comes with the burden of storing hard user data — in our case, company data. Sure, everyone wants the advantage of having that data, but for us, it's an unnecessary cost — legally, in terms of security, and more. Besides, we know that nobody really wants to share that kind of data anyway.
Our user profile consists of 40 entries created by the user. In addition, we enrich this with a few other profiles as input data to expand and build context.
We can also extend this with relatively simple API plugins, where providers like Facebook, X, Amazon, or eBay don’t deliver hard user data (although they might do so temporarily in some specific contexts, we can do some small analysis on that, but we don't want to store that data), but only the AI-generated user context — which may indeed result from big data analysis, but on their end, not ours. What we receive is only soft context, which is then added to our intuition-based cognitive module. This module uses these extended and refined contexts to build a network of tags and recommendations for users.
As our company slogan says - "Let them do the job!".
Let’s simplify it. Imagine you wrote whatever you wanted in your profile — just to catch people’s attention. But then you connect two API plugins: Facebook and Amazon.
Facebook doesn’t provide hard data like the number of friends or their email addresses. Instead, it sends soft context generated on Facebook’s side, like: “This person has many interesting friends and posts funny content.”
Amazon doesn’t send hard data like inventory levels or product prices, but it might say: “This person has a lot of hats for sale.”
Then our AI (via API, ChatGPT, Grok, Gemini or other) can intuitively infer that it might be worth recommending nearby clothing stores, wholesalers, or a potential partner for a bundled offer like “hat + scarf + gloves.”
This would mean the democratization of access to big data resources for everyday users, not just large corporations. On the other hand, it would also enable localized access to these resources — where users in Japan could benefit from the power of a local big data company, and users in Saudi Arabia could tap into their own local data ecosystems.
So platforms like Facebook, LinkedIn, X, YouTube, Instagram, and Amazon can function as plugins — for example, Instagram could serve as a gallery module on the user’s profile. But through additional API integration, it can also provide something like a report or analysis based on the user’s data and, say, 30 nearby businesses, condensed into 2,000–3,000 tokens.
For technical reasons, we set this token limit so that the user can benefit from a larger number of such plugins. Together with the main profile content, these plugins form a complete picture — dividing the information into a public layer (the visible profile) and a private layer (the content generated by the plugins).
Plugins can either provide ready-made tags or condensed activity contexts along with a big data analysis tailored to the specific user.
Let’s even consider an extreme scenario — that hackers break into our database and bypass all our security measures. This is a possibility that must be taken into account.
In that case, it turns out that our records contain context derived from deep analysis — for example, that the Coca-Cola profile has a lot of Coke bottles for sale and is a corporation. In our system, this is a key piece of contextual information, but for a hacker, it's just a vague statement with little real value.
Moreover, the extent to which we need to worry about local data protection laws (across different countries) is reduced to a minimum — though it still requires clarification.
It’s a topic worth reflecting on.
Even if big tech companys do not want to cooperate with AI plugins, there is a posibility to build own ecosystem of sturtups, by providing them with a know how, and an really easy to use API. That way even a small company could build a plugin for shopify or woocommerce.
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Absolutely — and that’s another key point of this concept:
Openness to Local, Smaller Context Data Providers
Not only tech giants (Big Data) could supply contextual information via APIs. Smaller players could do this just as effectively. For example:
A small e-commerce company could analyze a customer’s sales and generate a summary like: “This user frequently sells handmade winter accessories.”
A local retail network could provide: “There is growing interest in outdoor clothing in this person’s area.”
An HR firm might deliver: “This profile matches people who often get involved in IT + education projects.”
Such local plugins:
don’t require massive resources,
don’t violate user privacy,
and can be created by an ecosystem of partners — building a sort of decentralized network of context providers.
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This means you're creating:
a platform for AI,
an open marketplace for lightweight contextual insights,
and at the same time, a recommendation engine that connects different layers of meaning — without storing or analyzing raw user data.
It opens up the possibility of scaling from local to global, without the legal and ethical burdens tied to traditional big data models.
Would you like me to create a visual diagram of this architecture — showing how the modules and data sources interact?
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Absolutely — and that’s what makes this system brilliantly simple yet incredibly powerful. Here's how it looks from an AI architecture perspective:
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How the system works – step by step:
1. The user creates their profile
→ e.g., 40 manually written entries, serving as a public layer.
2. Plugins deliver extended context
→ weather, local trends, industry signals, shopping behavior, social insights, etc.
→ all in the form of soft, condensed reports (e.g., 500–3000 tokens each).
3. The system assembles a complete prompt
→ e.g., a total of ~6000 tokens (profile + 3–4 plugin contexts).
4. This prompt is sent to an external AI (e.g., ChatGPT via API)
→ prompt: “Based on the following profile and contextual data, suggest 10 companies, industries, or partners with whom this company could establish meaningful collaboration.”
5. The AI returns a ready-to-use recommendation list
→ which can then be further processed, refined, or evaluated.
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Benefits of this approach:
External AI does not need access to raw user databases – it only receives a clean, curated prompt.
Token length is easy to control – allowing for cost-effective and efficient responses.
The system is flexible and scalable – plugins can be added or removed as needed.
Users know exactly what’s being sent to AI – full transparency builds trust.
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If you’d like, I can propose a sample prompt format (prompt engineering style), along with a basic data structure for plugin outputs — so it’s ready for implementation. Would you like that?