1. Please introduce the founding background of XerpaAI. As a part of the UXLINK ecosystem, how does XerpaAI place itself because the “world’s first AI Development Agent”, and what’s its core mission? Within the Web3 area, what ache factors exist in conventional development fashions (similar to handbook advertising and marketing and KOL collaborations), and the way does XerpaAI remedy these issues via AI?
A: The institution of XerpaAI originated from the UXLINK ecosystem. We noticed that Web3 startups face important challenges when it comes to development, similar to high-cost handbook advertising and marketing, inefficient collaborations counting on KOLs, and fragmented consumer acquisition. Because the world’s first AI Development Agent (AGA), our core mission is clever development, serving to WEB3 startups shift from handbook operations to an clever and self-driven growth mannequin. The ache factors of conventional development fashions embody: excessive advertising and marketing budgets (international know-how firms spend 600 billion to 1 trillion US {dollars} yearly on development), subjective and time-consuming KOL matching, and problem in scaling group interactions. XerpaAI addresses these points via AI-driven content material era, clever distribution, and real-time optimization. For instance, it mechanically generates multilingual content material and distributes it via a community of over 100K KOCs/KOLs on platforms similar to X, Telegram, and TikTok, reaching a 3x enhance in conversion charges and a 70% discount in prices.
2. XerpaAI’s core idea is the “clever development engine”. Does this imply it may well fully substitute human development groups? Contemplating 2025 AI developments, such because the autonomous agent mannequin of agentic AI, how do you view XerpaAI’s position in serving to startups transition from “handbook growth” to “clever self-drive”?
A: Sure, our core idea is to construct an “clever development engine” that may considerably cut back reliance on human development groups, however not fully substitute them — as an alternative, it serves as an enhancer, permitting groups to deal with technique slightly than execution. In 2025, the rise of agentic AI endows AI brokers with stronger autonomy, and XerpaAI is a manifestation of this pattern: it acts like an clever Sherpa information, autonomously dealing with consumer habits evaluation, incentive triggering, and marketing campaign changes, serving to startups transition from “handbook growth” to “clever self-drive”.
3. What’s XerpaAI’s technical structure? How does it combine AI fashions (similar to content material era and real-time optimization) with Web3 native components (similar to link-to-earn mechanisms and social graphs) to assist undertaking development?
A: XerpaAI’s technical structure is a extremely modular multi-AI Brokers system designed to deal with complicated duties in Web3 development, similar to automated consumer acquisition, group growth, and KOL/KOC matching. We have now constructed the whole system as a collaborative agent community, the place every agent focuses on particular subtasks however collaborates seamlessly via shared states and communication protocols (similar to blockchain-based sensible contract verification). It is a type of multi-agent agentic workflows, the place brokers can autonomously plan, execute, and optimize motion paths, thereby reaching an end-to-end clever development engine.
At its core, XerpaAI’s structure revolves round a central AGA (AI Development Agent) coordinator that oversees the interactions of a number of devoted brokers, forming a dynamic decision-tree construction. The next is an in depth breakdown from the attitude of multi-AI Brokers:
Composition of the agent community:
– Planning Agent: That is the entry level, liable for decomposing high-level development objectives (similar to “growing consumer conversion charges for a DeFi undertaking”) into executable subtasks. It adopts the Plan-and-Remedy prompting technique, a sophisticated zero-shot reasoning methodology that first formulates a complete plan (for instance, dividing duties into content material era, KOL matching, and efficiency optimization) after which solves every subtask step-by-step. This methodology addresses the lacking steps difficulty of conventional Zero-Shot Chain-of-Thought (CoT), guaranteeing that the agent doesn’t skip key reasoning hyperlinks. For instance, when dealing with a WEB3 viral advertising and marketing process, the planning agent will first plan:
“Step 1: Analyze the target market;
Step 2: Generate multimodal content material;
Step 3: Match platform-specific KOLs;
Step 4: Monitor real-time suggestions.”
– Knowledge Assortment Agent: Accountable for real-time assortment and preprocessing of multi-source knowledge from the Web3 ecosystem (similar to blockchain transactions, social graphs, cross-platform consumer interactions). Knowledge sources embody X, Telegram, on-chain actions (similar to sensible contract interactions), and the social graph of the UXLINK ecosystem. Because the enter layer of the multi-agent system, the info assortment agent gives real-time, structured knowledge streams for different brokers (planning, content material era, distribution, optimization, integration), guaranteeing that choices are primarily based on the newest insights. For instance, it extracts interplay developments from over 110K communities for the planning agent to decompose duties.
– Content material Era Agent: Focuses on creating multilingual, multimodal content material (similar to textual content, photographs, and movies). It makes use of Zero-Shot Chain-of-Thought prompting by including “Let’s assume step-by-step” to induce step-by-step reasoning, similar to deriving personalised narratives from consumer knowledge with out the necessity for pre-trained examples. This permits the agent to generate high-quality content material in a zero-shot setting, supporting cross-platform distribution (similar to X, Telegram, and TikTok).
– Distribution & Matching Agent: Handles clever matching and content material distribution inside the 100K+ KOL/KOC community. It integrates Web3 native components similar to social graph evaluation and link-to-earn mechanisms, utilizing multi-agent collaboration to optimize paths — for instance, decomposing the matching course of via Plan-and-Remedy into “planning a listing of potential KOLs, then fixing compatibility and incentive allocation”.
– Optimization & Suggestions Agent: Displays efficiency indicators (similar to conversion charges and prices) in real-time and adjusts methods via self-reflection loops. It运用 Zero-Shot CoT to research knowledge biases, similar to step-by-step reasoning “If the conversion charge is decrease than anticipated, why? Step 1: Verify content material relevance; Step 2: Consider KOL affect; Step 3: Alter incentives”, thereby reaching a 70% price discount and a 3x enhance in conversions.
– Integration Agent: Bridges AI and Web3 elements, guaranteeing decentralized verification (similar to knowledge privateness on the blockchain) and cross-track assist (DeFi liquidity incentives, SocialFi group constructing).
Multi-agent collaboration mechanism:
Agent communication is achieved via a shared information graph primarily based on GraphRAG know-how, permitting real-time knowledge ingestion and reasoning. The central coordinator makes use of an A* search-inspired algorithm to navigate the motion area, avoiding inefficient paths and guaranteeing environment friendly execution.
We have now integrated Plan-and-Remedy because the core reasoning engine to beat the constraints of Zero-Shot CoT (similar to calculation errors or semantic misunderstandings). For instance, in a SocialFi undertaking, the planning agent first formulates a plan: “Subtask 1: Establish goal communities; Subtask 2: Generate interactive content material; Subtask 3: Distribute and optimize”, after which every agent makes use of Zero-Shot CoT to resolve them step-by-step, avoiding reliance on handbook examples.
This multi-agent system helps parallel processing and iterative studying: if one agent fails (such because the matching agent not discovering an acceptable KOL), the suggestions agent triggers a mirrored image loop to re-plan the trail. This design follows multi-agent developments, similar to inter-agent educating and optimization in simulated environments.
Recollections assist:
XerpaAI enhances the educational and adaptive capabilities of the multi-agent system via a Recollections mechanism (primarily based on long-term context storage), storing historic duties, consumer preferences, and optimization outcomes, much like a “near-infinite reminiscence” structure. This permits brokers to reuse information throughout duties and constantly enhance.
Recollections are saved in a distributed information graph (primarily based on GraphRAG) mixed with a vector database (Milvus) to assist environment friendly retrieval. Every agent (planning, content material era, distribution, optimization, knowledge assortment) shops key choices and leads to Recollections, similar to “A undertaking’s KOL matching elevated conversion charges by 3x, and high-interaction KOLs must be prioritized”.
As a shared useful resource, Recollections promote collaboration between brokers. The information assortment agent shops new knowledge in Recollections, the content material era agent adjusts its creations accordingly, the distribution agent optimizes KOL matching, and the optimization agent evaluates efficiency, forming an adaptive loop.
Recollections endow the system with “reminiscence”, enabling brokers to study historic patterns and optimize future duties. For instance, after a failed viral advertising and marketing marketing campaign for a WEB3 undertaking, Recollections document the explanations for failure (similar to inadequate incentives), and the planning agent adjusts the inducement mechanism for brand spanking new campaigns accordingly.
The essence of XerpaAI’s Recollections is to construct an exterior mind for XerpaAI’s customers, remodeling fragmented information into reusable structured recollections via hierarchical storage, dynamic indexing, and MCP protocols.
Total, this structure makes XerpaAI greater than only a device however an adaptive development associate that has served over 110K communities. Via the collaboration of multi-AI Brokers, coupled with superior prompting applied sciences similar to Plan-and-Remedy and Zero-Shot Chain-of-Thought, now we have achieved environment friendly, zero-shot automation of Web3 development. When you have particular process examples, I can additional show how these elements are utilized.
4. Within the 2025 AI breakthroughs, small specialised fashions and inference time computing have gotten focal factors. Has XerpaAI adopted comparable applied sciences to deal with huge quantities of knowledge (similar to 100K+ KOL matching and cross-platform distribution, together with X, Telegram, and TikTok)? How does its knowledge evaluation engine guarantee real-time suggestions and self-optimization?
A: Sure, now we have adopted small specialised fashions to deal with particular duties similar to KOL matching and cross-platform distribution. These fashions are optimized for Web3 knowledge to cut back inference time. In step with the 2025 pattern of inference time computing, our engine makes use of environment friendly algorithms to course of huge quantities of knowledge, similar to real-time matching from over 100K KOLs and distribution throughout X, Telegram, and TikTok. The information evaluation engine ensures self-optimization via machine studying loops: gathering consumer interplay knowledge, making use of reinforcement studying to regulate methods, and avoiding overfitting.
5. XerpaAI has served over 110K communities. How does it make the most of multimodal AI (combining textual content, photographs, and social knowledge) to automate consumer acquisition and group interplay? In contrast with present AI developments similar to near-infinite reminiscence and customized silicon, what are XerpaAI’s improvements in edge computing or cloud integration?
A: XerpaAI makes use of multimodal AI to course of textual content, photographs, and social knowledge, similar to producing image-enhanced content material or analyzing social graphs to automate interactions, and has served over 110K communities. In contrast with 2025 developments similar to near-infinite reminiscence, now we have innovated in cloud integration through the use of distributed computing to course of large-scale knowledge; when it comes to edge computing, now we have optimized cellular brokers to make sure low-latency interactions, similar to real-time responses to consumer queries in Telegram teams.
6. XerpaAI has a community of over 100K KOLs/KOCs. How does it serve these influencer teams via AI instruments (similar to personalised content material era and incentive optimization) to assist them enhance monetization effectivity and group interplay, thereby establishing a mutually useful channel benefit? Contemplating 2025 AI developments similar to personalised brokers, how do you assume it will amplify the viral unfold of Web3 initiatives?
A: XerpaAI’s 100K+ KOL/KOC community is the core of our channel benefit. Via AI instruments similar to personalised content material era and incentive optimization, we offer tailor-made providers to those influencers to assist them enhance monetization effectivity and group interplay. For instance, our AGA engine makes use of multimodal AI to generate unique content material (similar to photographs, video scripts, or posts focusing on particular audiences) and maximizes their earnings via real-time incentive optimization (similar to dynamically adjusting income sharing ratios primarily based on interplay knowledge) — this could enhance KOLs’ monetization effectivity by 2-3 occasions whereas enhancing group stickiness, similar to automated replies and gamified interactions. The result’s mutual profit: influencers acquire extra publicity and income, whereas we broaden our distribution channels via their networks. Within the 2025 AI developments, personalised brokers (similar to customized AI assistants) are dominating the influencer financial system, and XerpaAI is a pioneer on this software — our brokers can autonomously study KOL preferences and predict developments, thereby amplifying the viral unfold of Web3 initiatives. For instance, in a DeFi marketing campaign, via KOCs’ micro-sharing chains, exponential consumer development may be achieved, with conversion charges growing by greater than 5 occasions.
7. When serving KOLs/KOCs, what methods has XerpaAI adopted to make sure knowledge privateness and honest income sharing (similar to via blockchain-verified link-to-earn mechanisms) to domesticate long-term loyalty? How does this channel benefit translate right into a aggressive barrier for startups, particularly in multi-platform distribution (similar to X, Telegram, and TikTok)?
A: When serving KOLs/KOCs, we prioritize Web3-native methods to make sure knowledge privateness and honest income sharing: all interplay knowledge is verified via the blockchain (similar to utilizing zero-knowledge proofs to retailer anonymized data) to stop leakage; the link-to-earn mechanism mechanically executes income sharing primarily based on sensible contracts, guaranteeing transparency and on the spot funds (similar to token rewards primarily based on interplay metrics), which cultivates long-term loyalty — our retention charge exceeds 85%. This channel benefit interprets right into a aggressive barrier for startups: in multi-platform distribution (similar to real-time tweets on X, group interactions on Telegram, and quick movies on TikTok), our community kinds a “moat”, offering unique entry and optimized paths, serving to enterprises bypass conventional promoting bottlenecks and obtain low-cost, high-efficiency development. For instance, a WEB3 undertaking coated 5 million customers in 3 weeks via our KOL/KOC channels, whereas opponents wanted a number of months.
8. In 2025, with the rise of AI brokers, knowledge privateness and algorithmic bias are key challenges. As a Web3 & AI-native platform, how does XerpaAI guarantee transparency and decentralization (similar to via blockchain verification)? What are its issues concerning AI ethics?
A: Knowledge privateness and algorithmic bias are essential. As a Web3 & AI-native platform, we guarantee transparency via blockchain verification, similar to utilizing decentralized storage to guard consumer knowledge and conducting equity audits to keep away from bias. Our AI moral issues embody: anonymization of all mannequin coaching knowledge, user-controllable opt-out mechanisms, and common third-party audits to adjust to regulatory developments.
9. XerpaAI lately secured $6 million in seed funding, led by UFLY Capital. How will this funding be used for growth? Please share a particular case, similar to the way it helped a Web3 startup obtain development from scratch, highlighting its position in consumer acquisition and group constructing.
A: This $6 million seed funding can be used for product iteration, worldwide growth (similar to workforce recruitment in Silicon Valley, Tokyo, and Singapore), and ecosystem integration. A typical case is our help to a Web3 startup: ranging from scratch, our AGA generated multilingual content material, distributed it via the KOL community, constructed a group graph, and in the end acquired 100,000 customers inside one month, with group exercise growing by 2 occasions. This highlights our position in consumer acquisition and group constructing.
10. Trying to the longer term, how will XerpaAI combine into broader AI developments similar to personalised AI brokers or automated funding? What are the corporate’s subsequent technical iteration plans? What recommendation do you could have for AI entrepreneurs to deal with the dynamic modifications in Web3 development?
A: Sooner or later, XerpaAI will combine into the pattern of personalised AI brokers, similar to customized development paths, and discover automated funding modules. The subsequent iteration contains enhancing multimodal capabilities (similar to video era) and deeper Web3 integration. Recommendation for AI entrepreneurs: deal with ache factors similar to development automation, embrace agentic AI, and construct ecosystem partnerships to deal with the dynamic modifications in Web3 — for instance, monitor real-time developments and iterate shortly. XerpaAI’s service capabilities may also empower KOLs/KOCs, enabling this group to reinforce their respective affect with the assistance of XerpaAI.
11. As CTO, what’s your best expectation for the combination of AI and Web3? How does XerpaAI assist extra startups “join, broaden, and dominate the market”? Lastly, what would you prefer to say to potential companions or customers?
A: As CTO, my best expectation for the combination of AI and Web3 is to appreciate a very decentralized clever financial system, the place AI Brokers similar to XerpaAI drive clever development. XerpaAI will assist extra startups “join, broaden, and dominate the market” via our AGA engine, offering end-to-end assist from content material to optimization. Lastly, to potential companions and customers: be part of us to hurry up your development — welcome to go to xerpaai.com to strive it out, or DM us to debate cooperation!
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