Keep talent supply
ahead of store growth

SurSpark is a Shanghai-based AI recruiting technology company building an AI-powered workforce supply chain. We help service businesses develop a reliable talent supply that can keep pace with store growth.

Rapid Arrival is SurSpark’s core solution for full-time frontline hiring across multi-location service brands. It covers requirement discovery, talent search, screening, outreach, interview follow-up, arrival delivery and post-start retention tracking—and remains accountable for the arrival outcome.

Instead of asking store managers to post jobs, screen résumés, chase candidates and handle no-shows, Rapid Arrival uses AI to rebuild the workflow. Managers only need to submit the requirement, conduct the interview and arrange the start.

SurSpark currently serves more than 50 restaurant brands, including KFC, Banu Hotpot and Yun Hai Yao. We have generated over 10,000 interview referrals and built a dynamic pool of more than 50,000 workers across service, greeting, food running, kitchen and beverage roles.

Restaurants depend heavily on people. As a brand adds stores, hiring demand becomes fragmented across cities and trade areas and can spike with new openings, employee turnover and holiday traffic. Annual turnover in frontline restaurant roles often exceeds 80%, leaving many stores in a constant cycle of vacancy, hiring and renewed vacancy.

Frontline candidates may not have polished résumés or know how to express service awareness, personality and adaptability on paper. Conventional hiring overweights credentials and keywords, so people who genuinely fit the role can remain invisible.

Candidates also may not fully understand workload, scheduling, compensation or management expectations before starting. Employers struggle to find suitable people while workers repeatedly move through applications, starts and departures.

We began with restaurant chains to address both sides: help stores find people who truly fit, and help more workers be recognized and find roles where they can grow steadily.

China’s restaurant industry is entering a faster phase of chain expansion. The chain penetration rate reached 25% in 2025, up from 15% five years earlier, and Gaoyan Technology forecasts that it could reach 40% within another five years.

Central kitchens, cold-chain logistics, prepared-food infrastructure, digital tools and AI have made products, processes and remote store management increasingly repeatable. People have not become equally easy to replicate.

Every new location still needs a team capable of executing standards and operating reliably. Smaller formats and satellite stores use leaner teams, so each employee carries more responsibility and expectations for capability and stability rise.

That is the real bottleneck: when talent supply cannot keep pace with store growth, even mature products and operating systems cannot be implemented consistently.

Recruiting has traditionally relied on the judgment of experienced individuals. Those insights are difficult to standardize or reproduce across more cities and stores.

Our core team brings experience from Tsinghua University, Renmin University, Meituan, Ele.me, Lenovo and Innovation Works, spanning AI engineering, product, chain operations, recruiting and workforce supply. We turn tacit recruiting knowledge into three repeatable system capabilities: judgment, execution and dispatch.

Judgment converts vague preferences into explicit assessment standards. Large language models work with competency models, competency dictionaries and behavioral interviewing to evaluate capability, motivation and stability—and support one standard per store, one process per role and one profile per person.

Execution reliably completes repetitive recruiting work. Multi-agent workflows handle outreach, résumé screening, interview invitations, scheduling, follow-up and attendance reminders until candidates actually arrive.

Dispatch means talent does not need to be found from scratch after a vacancy appears. A dynamic network keeps candidate intent, preferences and availability current, then matches location, role requirements, capabilities and arrival probability for advance preparation and on-demand allocation.

Traditional recruiting often ends when a candidate starts. For SurSpark, every delivery result becomes evidence for the next match.

Whether candidates accept or reject a role, attend an interview, pass the store interview and remain after starting helps the system learn who fits a brand, which capabilities affect performance and which conditions lead to no-shows or turnover.

That evidence continuously updates role profiles, candidate profiles and delivery strategies. The system learns real store requirements, refreshes candidate capability, preferences, location and availability, and adjusts channels, screening questions, communication and follow-up timing.

More precise with every use means each delivery reduces uncertainty in the next one—helping employers find suitable people faster and helping workers find jobs that genuinely suit them.