Sprounix Primary Function: AI Tus Sawv Cev Hauj Lwm los ntawm Xiaoshi Dai, Di Xia and Feifan Gao

Qhov qauv tsim Sprounix Primary Function: AI Tus Sawv Cev Hauj Lwm los ntawm Xiaoshi Dai, Di Xia and Feifan Gao yog tus yeej muaj nqis heev ntawm qhov khoom plig 2026 A' Design Award Hlau hauv Pawg Sib Tw Tsim Khoom Digital.

HlauKhoom Digital· 2026

Sprounix Primary Function: AI Tus Sawv Cev Hauj Lwm

los ntawm Xiaoshi Dai, Di Xia and Feifan Gao

Tus Neeg Siv: Sprounix

Tus Yeej A' Design Award Hlau hauv Pawg Khoom Digital

5,263

Tus Yeej Khoom Plig Tau Txheeb Xyuas

Txheeb Xyuas Qhov Xwm Txheej Khoom Plig rau #183302

Mus Saib Lub Vev Xaib Saib ntawm A' Design Award Tsab Xov Xwm Daim Phiaj Khoom Plig

Xim

Qhov Tsim

Txog Sprounix Primary Function: AI Tus Sawv Cev Hauj Lwm

Book Text: Sprounix yog ib lub interface uas siv AI los ua kom yooj yim rau cov txheej txheem thov hauj lwm uas nyuaj heev los ntawm kev sib tham UX. Txhawm rau daws qhov teeb meem ntawm kev nkees rau kev thov hauj lwm, qhov tsim no hloov cov foos uas tsis txav (static forms) los ua ib lub multi modal chat interface uas khaws cov soft skills los ntawm kev sib tham ntuj. Lub UI muaj ib lub Co pilot dashboard uas ntws zoo thiab muab kev qhia txog kev xam phaj (interview coaching) thiab kev taug qab hauj lwm yam pom kev meej tseeb hauv lub sij hawm tiag tiag. Los ntawm kev sib koom cov Agent to Agent protocols, lub system ua kom cov kev sib tham nram qab (backend negotiations) ua haujlwm tau ywj siab, tso cai rau cov neeg siv los tswj cov kev thov uas muaj zaus ntau los ntawm ib tug qauv kev sib cuam tshuam uas yooj yim thiab tsis muaj kev ntxhov siab.

Qhov Lees Paub

Txog A' Design Award Hlau

Sprounix Primary Function: AI Tus Sawv Cev Hauj Lwm yog tus yeej ntawm A' Design Award Hlau, ib qho kev lees paub muaj meej mom uas muab rau cov qauv tsim zoo uas ua tau raws li cov kev xav tau ntawm kev ua haujlwm thiab kev lag luam uas xav tau los ntawm cov khoom tsim zoo. Cov neeg yeej Hlau qhia txog kev txawj loj, kev paub tshwj xeeb siab, kev nkag siab ntawm cov kws tshaj lij thiab lub peev xwm tsim ntawm cov neeg tsim. Sprounix Primary Function: AI Tus Sawv Cev Hauj Lwm xav tias yuav yog ib qho khoom tsim uas neeg hwm, qhuas thiab hlub uas siv tau thiab tsim tshiab. Cov qauv tsim yeej A' Design Award Hlau no sib xyaw cov kev coj ua zoo tshaj plaws ntawm kev lag luam thiab muaj cov yam ntxwv siv tshuab txawj ntse txuas nrog kev paub thiab txuj ci. Sprounix Primary Function: AI Tus Sawv Cev Hauj Lwm tau txais kev hwm vim nws tag nrho thiab tsim los muab kev txhim kho rau lub neej uas pab ua rau lub ntiaj teb zoo dua.

Tom Qab Qhov Tsim

Pob Xov Xwm

Cov credit, cov neeg pab thiab cov ntsiab lus kev txawj ntse muab los ntawm tus tsim.

Yam Tshwj Xeeb: Sprounix is a chat-centric AI Career Agent that transforms job seeking into a multimodal interactive journey. Beyond static resumes, it uses text and video dialogue to uncover soft skills and aspirations. A key innovation is the Agent2Agent A2A technology, enabling the user’s agent to communicate directly with recruiter systems for hyper-efficient matching. It also provides automated applications and real-time interview coaching, acting as a proactive career advocate.
Qhov Loj & Ntim: Sprounix is a cloud-based, responsive web platform compatible with all modern browsers. It supports multi-modal inputs including WebRTC video, voice-to-text, and PDF parsing. The matching engine achieves over 85% accuracy by moving beyond keywords to multi-dimensional intent recognition. The system adheres to WCAG 2.1 accessibility and enterprise-grade security standards, ensuring full GDPR compliance for encrypted candidate data protection.
Cov Ntsiab Lus: AI Career Agent, Agent2Agent, Conversational UI, UX Design, Future of Work, Automated Recruitment, NLP, Multi-modal Interaction
Thev Naus Laus Zis Tsim Khoom: Built on a robust AI infrastructure using Large Language Models (LLM) for natural language understanding and multi-modal analysis. The core innovation is the Agent2Agent (A2A) communication protocol that streamlines data exchange between independent AI entities. The system features a responsive React-based front-end for seamless cross-platform use, integrated with real-time video processing and scalable microservices to handle high-frequency job matching and data sync.
Kev Sib Tw Tsim Qauv: The primary challenge was designing a seamless UX for the complex Agent2Agent backend. We had to ensure users felt in control while their AI agent performed proactive tasks like autonomous applications. We solved this by creating a highly intuitive, chat-first interface that provides transparent "Co-pilot" updates. Integrating multi-modal inputs (text and video) while maintaining a consistent, supportive AI persona across all platforms was a significant technical and design obstacle we overcame.
Kev Tshawb Fawb: Our research explored the Job Search Anxiety among early career talents and the inefficiencies of keyword-based recruitment. We used a mixed-methods approach, including more than fifty user interviews and data analysis of traditional ATS systems. The results showed that users provide forty percent more detailed skill data via natural dialogue than static forms. This led to the development of our Chat-centric discovery interface and the Agent2Agent matching protocol, which proved to reduce initial screening time by seventy percent in pilot tests.
Kev Txhawb Siab: The inspiration came from the challenging labor market faced by Gen Z. Traditional job hunting is often repetitive and discouraging. We wanted to create a "Co-pilot" that doesn't just list jobs but actively advocates for the user. By combining AI-driven matching with a friendly, gamified experience, we aimed to lower the anxiety of career-starting and make professional growth feel like a guided, empowered journey.
Lub Sij Hawm Ua Hauj Lwm: The project conceptualization started in January 2025 in the United States. Following intensive research and UX prototyping of the conversational engine, the beta version of the platform was developed and finalized in late 2025.
Txheej Txheem Ua Hauj Lwm: The experience centers on a conversational interface where users engage with the AI via text or video. This chat-first approach replaces traditional forms, building a deep professional profile. Once the agent understands the candidate's goals, it leverages A2A protocols to negotiate with recruiter agents and automate applications. The co-pilot remains active throughout, providing instant feedback and resume tailoring via a fluid UI.
Cov Tswv Cuab Pab: Xiaoshi Dai
Feifan Gao
Di Xia
Kev Qhuas Duab: Image & video design: Xiaoshi Dai, Di Xia, Feifan Gao