Unistory

Development of a Blockchain Solution for the Paynet payment System

Our client is Paynet, one of the largest payment systems in Uzbekistan. In 2024, Paynet reached a daily volume of 2 million transactions. We developed a proprietary platform for cryptocurrency operations and payments to help the company expand its range of products and services.

A Bit of Context

In Uzbekistan, cryptocurrency operations have been legalized, which is one of the reasons blockchain-based transactions are becoming increasingly popular among users. Each such project must meet several criteria, one of which is a mandatory KYC procedure. 

Integration with MyID

To implement KYC on the platform, we integrated with MyID, the national facial recognition system. 

Blockchain Networks and Assets

We implemented 4 popular networks and 8 assets: Tron network: USDT, TRX / Polygon network: USDT, POL / TON network: TON, NOT, USDT / Bitcoin network for BTC transactions.

Crypto via ATM

We enabled users to top up their cryptocurrency wallet through an ATM. The user simply inserts their card, deposits fiat currency, and receives the equivalent amount of cryptocurrency at the prevailing exchange rate.

Blocking Suspicious Transactions and Users

To detect suspicious transactions, we implemented an AML bot. If a user attempts to send assets to a wallet flagged for criminal activity, the transaction will be blocked. In such cases, the user’s account may also be suspended.

Monetization

The payment system earns commissions on transactions performed by users on the platform. The admin panel allows setting a commission rate for each type of transaction.

Project Development Plans

We plan to connect the service to a liquidity aggregator, allowing the client to manage assets more freely. For example, it will enable user transactions even if the service’s master wallets do not have sufficient funds.

Blockchain Service for Selling Tokens on the Pre-Market

The client approached us to create a Web3 service for trading tokens that have not yet been listed. Our task was to brainstorm, propose ideas and unconventional approaches, and then develop a platform with smart contracts.

How the Project Works

Early Bird is an OTC market that enables trading of tokens that have not yet been listed on major exchanges. Sellers on the platform trade commitments to transfer tokens. Smart contracts on Solidity provide both parties with the necessary guarantees. The platform's native currency is ETH.

What NFTs Do in the Project

In this project, NFTs serve as proof of the commitments exchanged between buyers and sellers. We unlock the potential of non-fungible token technology by using NFTs as both a designation and a guarantee of completed deals.

What Happens to Deals After Listing

So, the purchased token gets listed—what's next? The service administrator manually issues an announcement for the smart contracts associated with that token. From this moment, sellers must provide the tokens within 24 hours so that buyers can burn their NFTs and receive their funds. 

Monetization Model

The monetization model for this project is based on service commissions from all completed transactions: sellers create lots, buyers purchase them, and everyone trades with each other. The owner of Early Bird receives a percentage from each transaction.

AI-powered aggregator of clothing for Muslim women

The client set out to help Muslim women around the world by creating a convenient tool for selecting modest clothing on popular marketplaces. Using AI, we determine whether items from ASOS and Farfetch meet the needs of Muslim women and then collect them on the Scanjab platform. According to surveys, the target audience now finds clothing 5 times faster.

How the Project Idea Emerged

Modern fashion marketplaces contain thousands or even millions of items. For Muslim women, choosing suitable clothing is more challenging: they have to carefully review each product page to determine whether it meets these requirements. Our client set out to change this.

How Scanjab Works

A woman visits Scanjab and opens the catalog. There she finds items from ASOS and Farfetch — each product listed has already been checked for compliance with Muslim modesty criteria.

The Role of AI in the Project

Once a day, our script searches for new items on ASOS and Farfetch through their APIs. After that, an LLM selects the products that are suitable for Muslim women.

Monetization Model

Each item on Scanjab includes a referral link leading to its page on the marketplace. Users follow the link and purchase the item; through the affiliate program, the client receives a percentage of each sale.

Admin Panel Capabilities

A flexible admin panel allows changing the criteria for selecting items at any time. For example, if a new tag is added and some items no longer meet the requirements, they are automatically removed from Scanjab.

Multi-agent AI Assistant that Ensures the Privacy of User Data

Our goal was to develop a personal AI assistant with an advanced level of personalization: a chatbot that helps users choose products and services based on their preferences. Achieve high flexibility and scalability of the project through the collaboration of multiple AI agents.

How the Web Service Works

Self AI analyzes your queries and gradually forms a personalized profile — remembering what you like, where you are, what you're searching for, and what interests you. This data is used to provide highly relevant and personalized responses, as well as to find suitable information, products, and services online.

Monetization Model

The main monetization method of the project is a paid subscription. Depending on the chosen plan, users receive a different number of requests. On the free plan, the number of requests is limited to ten per day.

Affiliate System

We have set up an affiliate system that provides an additional monetization option for the project. The administrator can add an affiliate to the system — a company that partners with Self and provides referral links to its products and services. When a user searches for a product, they receive several links to different online stores. One of these links is a referral link leading to our affiliate's website.

Multi-Agent AI System

We use the Ollama framework, which allows routing requests to both commercial and open-source LLMs. This reduces request costs and enables quick switching between models without compromising response quality. The system runs multiple AI agents, each with its own role.

Calendar Integration

We've integrated the service with Google and Apple Calendars. Users can connect their preferred calendar so that their personal AI assistant is always aware of their plans and tasks.

Admin Panel

We've developed an admin panel where the service owner can access financial information about subscriptions and referral link clicks in just a few clicks. This includes detailed data, such as the number of clicks on links from a specific affiliate.