To be honest, I have always been skeptical about the enterprise applications of blockchain projects. Over the years, I have seen too many projects bragging about "empowering businesses" and "transforming industries," only to find that none have materialized; they are all beautiful visions on PPTs. But last month, a friend in the supply chain, Lao Zhang, told me that he used KITE's technology to help a client optimize their procurement process, saving enough costs for his team to pay salaries for six months. At that moment, I didn't believe him: "Are you bragging again?" But Lao Zhang directly tossed the client's ROI report in my face—procurement cycles shortened from 48 hours to 6 hours, costs reduced by 18%, and error rates dropped from 5% to 0.3%. This made me start to seriously examine KITE's actual progress in enterprise applications. After three weeks, I tracked five companies using KITE, from cross-border e-commerce to DeFi protocols, from logistics to gaming, and found that KITE might really be changing the business rules in certain industries. Let me start with these five real cases to see how far KITE's enterprise applications have come and why it might open up a new landscape in the B-end market.
The first company is a client of Lao Zhang, a cross-border e-commerce company in Shenzhen with a monthly GMV of $5 million. Their pain point is typical: procurement involves over 200 suppliers, spread across China, Southeast Asia, and Europe, and they need to handle thousands of SKUs for inquiries, comparisons, orders, and payments daily. Previously, they relied entirely on manual work, with 10 procurement staff working overtime until 11 PM every day, often making mistakes—incorrect pricing, mixed quantities, wrong suppliers chosen. Lao Zhang created an AI procurement system for them using KITE, centered around three AI agents: inquiry AI, comparison AI, and execution AI. The inquiry AI automatically sends inquiries to suppliers, the comparison AI selects the optimal solution based on price, delivery time, and quality ratings, and the execution AI automatically places orders and makes payments. The entire process is fully automated; humans only need to set procurement rules and review exceptions.
I visited their company in person, and the procurement manager, Xiao Wang, demonstrated the system for me. She set up a rule in the backend: "Procurement cost must not exceed 105% of the budget, delivery time must not exceed 7 days, and supplier ratings must not be below 4.5 stars." Then she clicked "Start AI Procurement," and the system began to work. I watched as the inquiry AI sent inquiries to 50 suppliers within 3 minutes (the traditional method takes half a day), and 5 minutes later received 42 quotes. The comparison AI immediately began analyzing—not just comparing prices, but also considering exchange rate fluctuations, logistics costs, and supplier historical performance. Ten minutes later, the comparison AI provided a recommended plan: procure 1000 pieces from Supplier A, 500 pieces from Supplier B, and 300 pieces from Supplier C. The execution AI automatically sent orders, completing payments through KITE's x402 protocol—each payment incurs a fee of $0.0003, confirmed in milliseconds. The entire process took 18 minutes, while previously it would take at least 8 hours.
Even more remarkable is the application of the PoAI mechanism. The contributions of these three AI agents are precisely quantified: the inquiry AI contacted 50 suppliers and earned 30% of the service fee; the comparison AI performed complex analyses and earned 50%; the execution AI performed simple tasks and earned 20%. This distribution is completely automatic. Xiao Wang said, "In the past, we couldn't even calculate the cost structure; now we know exactly where every penny goes. Clients have seen this transparency, and trust has significantly increased." More importantly, this system helped them survive in bear markets. Clients cut budgets, reducing procurement staff from 15 to 5, but business volume increased instead—because AI doesn't require a salary, doesn't make mistakes, and works 24/7. Xiao Wang told me that KITE's costs are only 1/10 of traditional labor, but its efficiency is three times higher.
The second company is a DeFi protocol that automates market making. Their pain point is that AI agents require frequent calls to on-chain data and execution of trades, but traditional blockchain gas fees and delays are unbearable. Their CTO, Lao Li, told me that previously they used Ethereum, and the AI had to call price data 50 times per second and execute 10 arbitrage trades, leading to gas fees of $2,000 a day, and they often missed arbitrage opportunities due to network congestion. Now, with KITE's state channel technology, the AI completes a large number of calculations and transactions off-chain, only settling on-chain when necessary. I looked at their data: the daily average AI call volume skyrocketed from 3,000 times (limited by cost) to 500,000 times, gas fees dropped from $2,000 to $15, and the success rate of arbitrage increased from 40% (due to delays) to 85%. The most astonishing is the profit—previously the daily average arbitrage income was $500, with only $200 left after costs; now the daily average income is $2,000, with costs at $15, resulting in a net profit of $1,985, nearly a tenfold increase.
Lao Li showed me the work logs of their AI agents, which helped me understand how significant KITE's technological advantages are. The AI agents are monitoring price discrepancies across multiple DEXs every millisecond, and once they discover an arbitrage opportunity (for example, ETH/USDC on Uniswap is 0.5% cheaper than on PancakeSwap), they immediately execute the trade through the state channel—buying on Uniswap and selling on PancakeSwap, completing the entire process in less than 200 milliseconds. Traditional solutions must wait for block confirmation, which takes at least 12 seconds, during which the price discrepancy may vanish. Lao Li said, "KITE's state channel allows our AI to truly perform high-frequency trading, which was unimaginable before." More importantly, KITE's Agent Passport grants their AI an independent credit record—after completing 100,000 transactions with an 85% success rate, this record is stored on-chain, and other protocols are willing to offer this AI higher limits and lower fees. This "AI credit system" could be the future direction of DeFi.
The third company is a logistics company that focuses on intelligent scheduling for cross-border logistics. Their CEO, Lao Zhao, said that the biggest pain point in logistics is the lack of transparency and high coordination costs. A cross-border order involves sea freight, air freight, land transport, customs, and warehousing, with at least 10 links and 20 participants. Previously, they relied entirely on manual calls and emails for coordination, which was inefficient and error-prone. They created an AI logistics scheduling system using KITE, where each link has an AI agent—sea freight AI, air freight AI, customs AI, etc. These AIs automatically collaborate through KITE's Collaboration Protocol, sharing data in real time and automatically adjusting plans.
I followed them in processing an order for a batch of electronics from Shenzhen, China, to Los Angeles, USA. As soon as the order entered the system, the sea freight AI immediately began checking shipping schedules and available space, while the air freight AI looked for alternative flight options. The customs AI pre-prepared customs documents, and the warehousing AI booked the destination warehouse. The entire process involved automatic data transfer and fee payments among the AIs (via KITE's x402 protocol). The most amazing part was that when the sea freight AI discovered a 2-day delay in the scheduled shipping date, it automatically activated an emergency plan—switching to air freight, which, although 20% more expensive, ensured on-time delivery. This decision was made entirely autonomously by the AI. Lao Zhao said, "In the past, such situations would require a half-day meeting to discuss; now AI can resolve it in 5 minutes."
Lao Zhao showed me the comparison data before and after using KITE: order processing time was reduced from an average of 3 days to 8 hours, coordination costs dropped from $50 per order to $5 (AI automatically coordinates without the need for manual phone calls), and punctuality rates improved from 75% to 92%. More importantly, customer satisfaction—clients can see in real-time the location of goods, the progress of each link, and the estimated delivery time, which is a level of transparency traditional logistics cannot achieve. Lao Zhao said that KITE's PoAI mechanism allows him to precisely calculate the cost and value of each link, "Previously, logistics costs were a black box; now every penny is clear, and I can quote accurately, increasing the profit margin from 5% to 12%."
The fourth company is a GameFi project that creates AI NPC systems for blockchain games. Their technical director, Xiao Liu, said that traditional game NPCs are controlled by scripts, have fixed behavior patterns, and lack true intelligence. They want to create "living NPCs"—that can learn, trade, and make autonomous decisions. After using KITE, each NPC is an AI agent with its own Agent Passport (digital identity), wallet, and transaction history. When players trade items with NPCs, they are not trading with the system but with a "living" AI—AI adjusts prices according to market conditions, remembers players' transaction histories, and actively seeks arbitrage opportunities.
I tried their game demo and traded with a merchant NPC. This NPC actually bargained—when I offered 10 gold coins for a sword, it said, "The price of iron ore has risen recently; it now costs 12 gold coins." I replied, "That's too expensive," and it countered, "11 gold coins, but you have to help me run an errand to deliver a letter." This kind of dynamic interaction is something traditional games cannot achieve. Even more astonishing is that this NPC is conducting business within the game—buying players' junk equipment at low prices, repairing them, and selling them at high prices, using the profits to expand inventory. Xiao Liu said that some NPCs have already "struck it rich," with assets exceeding those of ordinary players. This "AI wealth creation" gameplay adds a significant level of fun and depth to the game.
Xiao Liu showed me the backend data: the daily trading volume of AI NPCs in the game averages 5000 transactions, with a total transaction value of $20,000 (in-game currency). KITE charges a 0.3% fee, which amounts to $60. Although it is not much, it should be noted that this is just a small game with 200 NPCs. If scaled up to 10,000 NPCs and 1 million players, the daily transaction value could reach $1 million, with KITE earning $3,000 daily and an annual income of $1.09 million. Xiao Liu said that the value of KITE lies not only in technology but also in the business model—"Previously, in-game transactions were all about platform take rates; now AI NPCs can also make money and need to pay taxes (fees). This concept of an 'AI economy' could be the future of GameFi."
The fifth company is an AI financial services firm that offers intelligent investment advisory. They use a combination of KITE and Minara to provide AI-driven asset allocation services to clients. Clients only need to tell the AI their risk preferences and investment goals, and the AI automatically analyzes the market, selects assets, executes trades, and regularly rebalances. CEO Lao Sun said that traditional investment advisors are either too expensive (private banking service fees are annualized at 2%) or too simplistic (robo-advisors just have fixed allocations). Their AI investment advisory combines the advantages of both: high intelligence (using GPT-4 to analyze the market), low cost (the AI doesn’t require a salary), and high transparency (every transaction can be traced).
I looked at the investment report they provided to clients, and it was indeed professional. The AI analyzes not only the macro economy, industry trends, and individual stock fundamentals but also considers the client's personal situation—such as if a client says, "I need to reserve $200,000 in cash to buy a house next month," the AI will automatically adjust the allocation to reduce the proportion of illiquid assets. During execution, the AI automatically completes transactions and payments through KITE, and the PoAI mechanism records each decision contribution of the AI—macro analysis AI, stock selection AI, and trade execution AI each receive corresponding service fees. Lao Sun said that KITE's transparency makes clients particularly assured, "Clients can see what each AI has done and how much it has charged, unlike traditional advisors where fees are confusing."
Lao Sun showed me the performance data: the managed asset scale grew from $500,000 at the beginning of the year to $3 million now, the number of clients increased from 5 to 45, and the average annualized return is 12% (after fees), with a client retention rate of 95%. The key point is the cost structure—traditional advisory services have human labor costs accounting for 60% of revenue, whereas they only account for 15% (mainly AI computing costs and KITE fees). This cost advantage allows them to charge lower management fees (annualized 0.8% vs. the traditional 2%), attracting a large number of middle-class clients. Lao Sun said, "KITE + Minara enables us to provide 'inclusive wealth management'; services that only the wealthy enjoyed before are now accessible to the middle class."
By organizing these five cases, I found several common advantages of KITE in enterprise applications. The first is cost advantage—the operational cost of AI agents is only 1/10 to 1/5 of that of human labor, while KITE's fees (0.1-0.5%) are far lower than traditional intermediaries (5-20%). The second is efficiency advantage—AI does not get tired, does not make mistakes, and works 24/7, while KITE's millisecond-level payment and state channel technology enable high-frequency operations. The third is transparency advantage—the PoAI mechanism makes every transaction and decision traceable, allowing companies to accurately calculate costs and value. The fourth is credit advantage—the Agent Passport establishes credit records for AI; the better the AI performs, the higher its credit, and the more resources it can acquire—this kind of positive cycle is not achievable in traditional systems.
However, KITE's enterprise applications also face some challenges. The first is regulatory uncertainty—how to define legal responsibilities when AI agents autonomously conduct financial transactions? If an AI error leads to losses, who compensates? The current legal framework hasn't caught up. The second is technological maturity—while the Ozone testnet data is promising, enterprise applications require extremely high stability and security, and whether it can withstand large-scale testing after the mainnet launch remains unknown. The third is market education—most enterprises still do not understand the AI agent economy and need time and cases to prove the value. The fourth is competitive pressure—if giants like Coinbase and Binance launch similar services, can KITE maintain its advantage?
But even with these challenges, I still have high hopes for KITE's prospects in the B-end market. The reason is simple: the bear market has made businesses more pragmatic, focusing more on cost reduction and efficiency improvement rather than speculative concepts. KITE addresses real issues—high payment costs, low coordination efficiency, and poor transparency—pain points that businesses face in any economic cycle. More importantly, the enterprise cases accumulated by KITE in the bear market are the most difficult assets to replicate. When the bull market comes, these success stories will become the best marketing material, attracting more businesses to enter.
From the feedback of the five companies, KITE's greatest value is not in how cool the technology is, but in its ability to genuinely help businesses save money and make profits. Lao Zhang said KITE helped his client save 18% in costs, Lao Li said the profits of the DeFi protocol increased tenfold, Lao Zhao said the logistics profit margin rose from 5% to 12%, Xiao Liu said GameFi found a new business model, and Lao Sun said wealth management can achieve inclusivity. These are all tangible business values, not visions on PPTs. If KITE can replicate these cases across more industries, it could truly open up a new landscape in the B-end market—not by disrupting a certain industry, but by becoming the infrastructure of the AI agent economy, permeating various sectors. For businesses, KITE is not an option but a necessity—just like today's businesses must use electricity and the internet, future enterprises may have to use AI agents and KITE payments.
