 |
Singdata Closes Series B at over USD $100M, Launches in Hong Kong with a Bet on "AI-Native Data Infrastructure" — Aligned with the HKSAR Government’s HKD 3 Billion "AI+" Strategy
HONG KONG, June 9, 2026 /PRNewswire/ — Enterprises worldwide are racing to deploy AI Agents. Yet according to 2026 reports from Deloitte and Google Cloud, only 31% have reached production. 46% of organizations cite systems integration as the top barrier, and 42% point to data access and quality. The bottleneck is no longer the model — it is whether the underlying data is AI-Ready.
Singdata (Yunqi Tech), an AI-native data infrastructure company, today announced from Hong Kong the close of its Series B funding round, bringing total funding to over USD $100 million. The milestone marks an inflection point for the Asia-Pacific "Data + AI" infrastructure category — a category where the international comparable, Databricks, has seen its valuation grow 3.1x in the past 18 months to USD $134 billion.
Why Hong Kong, Why Now — After Compute, Data Is the Next Battlefield
The HKSAR Government is driving a HKD 3 billion "AI+" strategy: a HKD 1 billion Hong Kong AI Research and Development Institute (HKAIRDI), the Sandy Ridge supercomputing centre breaking ground in 2026, and citywide compute capacity expanding 36-fold by 2032. Hong Kong is positioning itself as a dual data hub between mainland China and the international market.
"Once compute becomes abundant, the next bottleneck is the data layer that powers AI applications." Singdata’s Hong Kong launch is a direct response to that thesis — when enterprises already have compute and mature models, what determines whether AI works in production is a paradigm upgrade in data infrastructure. Singdata’s recently published technical thesis, Five Design Principles of an AI-Native Data Platform, sets out the technical path and design principles behind this paradigm shift in full.
Solving Two Structural Bottlenecks for Enterprise AI
Singdata’s core innovations — Generic Incremental Compute (GIC) and a Lakehouse architecture — address the two structural pain points of enterprise AI deployment:
- AI-Ready data: Native support for documents, audio, video, chat logs and other unstructured "dark data" that constitutes over 80% of enterprise data — automatically parsed, vectorized, and aligned with LLM inference.
- Real-time, low-cost retrieval: GIC processes only the data that changes, decisively breaking the cost-and-latency ceiling of traditional Lambda architectures’ "full-rerun" approach, and supporting the high-frequency, millisecond-level vector search that AI Agents require.
Singdata’s AI Lakehouse is compatible with all major cloud platforms, and Singdata is a major contributor to the Apache Iceberg open standard, ensuring customers retain 100% sovereignty and portability over their own data — strategically significant in an Asia-Pacific market where data sovereignty is fast becoming a board-level concern.
Customers: Cross-Industry, Cross-Geography Enterprise Validation
Singdata’s customer base spans Asia’s leading enterprises:
- Internet: AntGroup, Rednote, Kuaishou
- Automotive: Changan Auto, Great Wall Motor, Toyota, Volkswagen
- Logistics & Commerce: Ninja Van, Synagie, Atlas
The breadth of industries and geographies served is, in itself, the most direct proof that Singdata’s architecture adapts to highly differentiated enterprise environments.
Team: Alibaba "Apsara" Veterans, Combining Global Vision with Asia-Pacific Execution
Singdata’s founding team draws on senior alumni from Microsoft, Oracle, Alibaba Cloud, and ByteDance’s Volcano Engine — and was central to building Alibaba Cloud’s "Apsara" data platform, one of the largest cloud-native data systems globally. The team combines deep familiarity with global technical frontiers and large-scale Asia-Pacific operational experience.
Founder Statement
|
"The data stack enterprises built over the past two decades was designed for human analysts reading T+1 dashboards. An AI Agent can fire ten thousand queries a second. Run that workload on the old architecture and every Agent thought triggers an expensive full table scan — that is the real reason enterprise AI is failing to scale. What Singdata is rebuilding isn’t just the engine — it’s the paradigm: a data platform designed for Agents, not only for analysts." — Yu Sicheng, CEO, Singdata (Yunqi Tech) |
This funding round will be fully invested in continued R&D of Singdata’s AI-native data platform, accelerating its trajectory to become the platform of choice for Data + AI infrastructure across Asia-Pacific, with Hong Kong serving as its strategic gateway to international markets.
About Singdata (Yunqi Tech)
Founded in 2022, Singdata is an enterprise AI-native data infrastructure company. Its proprietary AI Lakehouse, powered by Generic Incremental Compute (GIC), bridges the last mile between enterprise data and AI — already trusted by leading organizations across Asia-Pacific including Rednote, Kuaishou, Toyota, Volkswagen, and Ninja Van. Singdata is a core contributor to the Apache Iceberg open standard and is committed to becoming the leading Data + AI infrastructure platform for Asia-Pacific.
Further Reading | Full Technical Thesis
Five Design Principles of an AI-Native Data Platform — Singdata’s systematic thesis on the paradigm shift in data infrastructure for the AI era, covering unified Lakehouse storage, AI as a native compute engine, the Medallion Architecture with incremental compute, Agent-friendly development paradigms, and enterprise-grade governance:
https://www.yunqi.tech/resource/blogs/ai-native-data-platform
Product demo & consultation: https://www.yunqi.tech/reservation
Source : The Real Bottleneck in the Age of AI Agents Isn't the Model — It's the Data
The information provided in this article was created by Cision PR Newswire, our news partner. The author's opinions and the content shared on this page are their own and may not necessarily represent the perspectives of Siam News Network.