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 Progress Agent”, and what’s its core mission? Within the Web3 subject, what ache factors exist in conventional development fashions (equivalent to guide advertising and marketing and KOL collaborations), and the way does XerpaAI clear up these issues by way of AI?
A: The institution of XerpaAI originated from the UXLINK ecosystem. We noticed that Web3 startups face vital challenges by way of development, equivalent to high-cost guide advertising and marketing, inefficient collaborations counting on KOLs, and fragmented consumer acquisition. Because the world’s first AI Progress Agent (AGA), our core mission is clever development, serving to WEB3 startups shift from guide operations to an clever and self-driven enlargement mannequin. The ache factors of conventional development fashions embrace: excessive advertising and marketing budgets (world know-how firms spend 600 billion to 1 trillion US {dollars} yearly on development), subjective and time-consuming KOL matching, and problem in scaling neighborhood interactions. XerpaAI addresses these points by way of AI-driven content material era, clever distribution, and real-time optimization. For instance, it mechanically generates multilingual content material and distributes it by way of a community of over 100K KOCs/KOLs on platforms equivalent to X, Telegram, and TikTok, reaching a 3x improve in conversion charges and a 70% discount in prices.
2. XerpaAI’s core idea is the “clever development engine”. Does this imply it will possibly fully change human development groups? Contemplating 2025 AI tendencies, such because the autonomous agent mannequin of agentic AI, how do you view XerpaAI’s function in serving to startups transition from “guide enlargement” 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 change them — as a substitute, it serves as an enhancer, permitting groups to concentrate on technique relatively than execution. In 2025, the rise of agentic AI endows AI brokers with stronger autonomy, and XerpaAI is a manifestation of this development: it acts like an clever Sherpa information, autonomously dealing with consumer conduct evaluation, incentive triggering, and marketing campaign changes, serving to startups transition from “guide enlargement” to “clever self-drive”.
3. What’s XerpaAI’s technical structure? How does it combine AI fashions (equivalent to content material era and real-time optimization) with Web3 native components (equivalent to link-to-earn mechanisms and social graphs) to help venture development?
A: XerpaAI’s technical structure is a extremely modular multi-AI Brokers system designed to deal with advanced duties in Web3 development, equivalent to automated consumer acquisition, neighborhood enlargement, and KOL/KOC matching. We’ve got constructed all the system as a collaborative agent community, the place every agent focuses on particular subtasks however collaborates seamlessly by way of shared states and communication protocols (equivalent to blockchain-based good contract verification). This can be 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 Progress 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 targets (equivalent to “growing consumer conversion charges for a DeFi venture”) into executable subtasks. It adopts the Plan-and-Remedy prompting technique, a sophisticated zero-shot reasoning technique 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 technique addresses the lacking steps situation 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: Chargeable for real-time assortment and preprocessing of multi-source knowledge from the Web3 ecosystem (equivalent to blockchain transactions, social graphs, cross-platform consumer interactions). Knowledge sources embrace X, Telegram, on-chain actions (equivalent to good contract interactions), and the social graph of the UXLINK ecosystem. Because the enter layer of the multi-agent system, the information assortment agent supplies real-time, structured knowledge streams for different brokers (planning, content material era, distribution, optimization, integration), guaranteeing that choices are based mostly on the newest insights. For instance, it extracts interplay tendencies from over 110K communities for the planning agent to decompose duties.
– Content material Technology Agent: Focuses on creating multilingual, multimodal content material (equivalent to textual content, photos, 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, equivalent to deriving customized narratives from consumer knowledge with out the necessity for pre-trained examples. This enables the agent to generate high-quality content material in a zero-shot setting, supporting cross-platform distribution (equivalent 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 equivalent to social graph evaluation and link-to-earn mechanisms, utilizing multi-agent collaboration to optimize paths — for instance, decomposing the matching course of by way of Plan-and-Remedy into “planning a listing of potential KOLs, then fixing compatibility and incentive allocation”.
– Optimization & Suggestions Agent: Screens efficiency indicators (equivalent to conversion charges and prices) in real-time and adjusts methods by way of self-reflection loops. It运用 Zero-Shot CoT to research knowledge biases, equivalent to step-by-step reasoning “If the conversion price is decrease than anticipated, why? Step 1: Examine content material relevance; Step 2: Consider KOL affect; Step 3: Modify incentives”, thereby reaching a 70% value discount and a 3x improve in conversions.
– Integration Agent: Bridges AI and Web3 parts, guaranteeing decentralized verification (equivalent to knowledge privateness on the blockchain) and cross-track help (DeFi liquidity incentives, SocialFi neighborhood constructing).
Multi-agent collaboration mechanism:Agent communication is achieved by way of a shared information graph based mostly 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’ve got included Plan-and-Remedy because the core reasoning engine to beat the constraints of Zero-Shot CoT (equivalent to calculation errors or semantic misunderstandings). For instance, in a SocialFi venture, the planning agent first formulates a plan: “Subtask 1: Determine 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 guide 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 tendencies, equivalent to inter-agent educating and optimization in simulated environments.
Reminiscences help:
XerpaAI enhances the training and adaptive capabilities of the multi-agent system by way of a Reminiscences mechanism (based mostly on long-term context storage), storing historic duties, consumer preferences, and optimization outcomes, just like a “near-infinite reminiscence” structure. This allows brokers to reuse information throughout duties and constantly enhance.
Reminiscences are saved in a distributed information graph (based mostly on GraphRAG) mixed with a vector database (Milvus) to help environment friendly retrieval. Every agent (planning, content material era, distribution, optimization, knowledge assortment) shops key choices and ends in Reminiscences, equivalent to “A venture’s KOL matching elevated conversion charges by 3x, and high-interaction KOLs must be prioritized”.
As a shared useful resource, Reminiscences promote collaboration between brokers. The information assortment agent shops new knowledge in Reminiscences, 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.
Reminiscences 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 venture, Reminiscences report the explanations for failure (equivalent to inadequate incentives), and the planning agent adjusts the inducement mechanism for brand new campaigns accordingly.
The essence of XerpaAI’s Reminiscences is to construct an exterior mind for XerpaAI’s customers, remodeling fragmented information into reusable structured reminiscences by way of hierarchical storage, dynamic indexing, and MCP protocols.
General, this structure makes XerpaAI greater than only a software however an adaptive development associate that has served over 110K communities. By way of the collaboration of multi-AI Brokers, coupled with superior prompting applied sciences equivalent to Plan-and-Remedy and Zero-Shot Chain-of-Thought, we’ve got achieved environment friendly, zero-shot automation of Web3 development. You probably have particular process examples, I can additional display how these parts are utilized.
4. Within the 2025 AI breakthroughs, small specialised fashions and inference time computing have gotten focal factors. Has XerpaAI adopted related applied sciences to deal with large quantities of information (equivalent 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, we’ve got adopted small specialised fashions to deal with particular duties equivalent to KOL matching and cross-platform distribution. These fashions are optimized for Web3 knowledge to cut back inference time. In keeping with the 2025 development of inference time computing, our engine makes use of environment friendly algorithms to course of large quantities of information, equivalent to real-time matching from over 100K KOLs and distribution throughout X, Telegram, and TikTok. The information evaluation engine ensures self-optimization by way of machine studying loops: amassing 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, photos, and social knowledge) to automate consumer acquisition and neighborhood interplay? In contrast with present AI tendencies equivalent 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, photos, and social knowledge, equivalent to producing image-enhanced content material or analyzing social graphs to automate interactions, and has served over 110K communities. In contrast with 2025 tendencies equivalent to near-infinite reminiscence, we’ve got innovated in cloud integration through the use of distributed computing to course of large-scale knowledge; by way of edge computing, we’ve got optimized cell brokers to make sure low-latency interactions, equivalent 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 by way of AI instruments (equivalent to customized content material era and incentive optimization) to assist them enhance monetization effectivity and neighborhood interplay, thereby establishing a mutually helpful channel benefit? Contemplating 2025 AI tendencies equivalent to customized brokers, how do you assume this may amplify the viral unfold of Web3 tasks?
A: XerpaAI’s 100K+ KOL/KOC community is the core of our channel benefit. By way of AI instruments equivalent to customized content material era and incentive optimization, we offer tailor-made providers to those influencers to assist them enhance monetization effectivity and neighborhood interplay. For instance, our AGA engine makes use of multimodal AI to generate unique content material (equivalent to photos, video scripts, or posts focusing on particular audiences) and maximizes their revenue by way of real-time incentive optimization (equivalent to dynamically adjusting income sharing ratios based mostly on interplay knowledge) — this may improve KOLs’ monetization effectivity by 2-3 occasions whereas enhancing neighborhood stickiness, equivalent to automated replies and gamified interactions. The result’s mutual profit: influencers acquire extra publicity and income, whereas we increase our distribution channels by way of their networks. Within the 2025 AI tendencies, customized brokers (equivalent to customized AI assistants) are dominating the influencer financial system, and XerpaAI is a pioneer on this utility — our brokers can autonomously study KOL preferences and predict tendencies, thereby amplifying the viral unfold of Web3 tasks. For instance, in a DeFi marketing campaign, by way of KOCs’ micro-sharing chains, exponential consumer development might 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 truthful income sharing (equivalent to by way of 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 (equivalent to X, Telegram, and TikTok)?
A: When serving KOLs/KOCs, we prioritize Web3-native methods to make sure knowledge privateness and truthful income sharing: all interplay knowledge is verified by way of the blockchain (equivalent to utilizing zero-knowledge proofs to retailer anonymized data) to forestall leakage; the link-to-earn mechanism mechanically executes income sharing based mostly on good contracts, guaranteeing transparency and instantaneous funds (equivalent to token rewards based mostly on interplay metrics), which cultivates long-term loyalty — our retention price exceeds 85%. This channel benefit interprets right into a aggressive barrier for startups: in multi-platform distribution (equivalent to real-time tweets on X, group interactions on Telegram, and quick movies on TikTok), our community varieties 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 venture coated 5 million customers in 3 weeks by way of 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 (equivalent to by way of blockchain verification)? What are its concerns concerning AI ethics?
A: Knowledge privateness and algorithmic bias are essential. As a Web3 & AI-native platform, we guarantee transparency by way of blockchain verification, equivalent to utilizing decentralized storage to guard consumer knowledge and conducting equity audits to keep away from bias. Our AI moral concerns embrace: anonymization of all mannequin coaching knowledge, user-controllable opt-out mechanisms, and common third-party audits to adjust to regulatory tendencies.
9. XerpaAI not too long ago secured $6 million in seed funding, led by UFLY Capital. How will this funding be used for enlargement? Please share a selected case, equivalent to the way it helped a Web3 startup obtain development from scratch, highlighting its function in consumer acquisition and neighborhood constructing.
A: This $6 million seed funding will probably be used for product iteration, worldwide enlargement (equivalent to group 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 by way of the KOL community, constructed a neighborhood graph, and in the end acquired 100,000 customers inside one month, with neighborhood exercise growing by 2 occasions. This highlights our function in consumer acquisition and neighborhood constructing.
10. Seeking to the longer term, how will XerpaAI combine into broader AI tendencies equivalent to customized AI brokers or automated funding? What are the corporate’s subsequent technical iteration plans? What recommendation do you will have for AI entrepreneurs to deal with the dynamic adjustments in Web3 development?
A: Sooner or later, XerpaAI will combine into the development of customized AI brokers, equivalent to customized development paths, and discover automated funding modules. The following iteration consists of enhancing multimodal capabilities (equivalent to video era) and deeper Web3 integration. Recommendation for AI entrepreneurs: concentrate on ache factors equivalent to development automation, embrace agentic AI, and construct ecosystem partnerships to deal with the dynamic adjustments in Web3 — for instance, monitor real-time tendencies and iterate rapidly. XerpaAI’s service capabilities may even empower KOLs/KOCs, enabling this group to boost their respective affect with the assistance of XerpaAI.
11. As CTO, what’s your biggest expectation for the combination of AI and Web3? How does XerpaAI assist extra startups “join, increase, and dominate the market”? Lastly, what would you wish to say to potential companions or customers?
A: As CTO, my biggest expectation for the combination of AI and Web3 is to appreciate a really decentralized clever financial system, the place AI Brokers equivalent to XerpaAI drive clever development. XerpaAI will assist extra startups “join, increase, and dominate the market” by way of our AGA engine, offering end-to-end help 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 attempt it out, or DM us to debate cooperation!
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