In its most recent round of funding, Taiwania Hive Ventures, with participation from Iterative and 500 Global, has raised $3 million for Decube, a data trust and context platform for enterprise artificial intelligence (AI) based in Malaysia.
As businesses increasingly seek to operationalize AI on top of reliable data, the company said in a statement on Wednesday that the funding will support its global expansion, ongoing product innovation, and quick expansion throughout the Asia Pacific (APAC) region.
Decube intends to use this funding to broaden its global reach and grow quickly throughout the Asia-Pacific area, where businesses are updating their data estates and getting ready for AI on a large scale.
In response to this increasing demand, the company will make investments in enterprise deployments, regional partnerships, and product capabilities.
“Decube was founded on a simple insight: enterprises can’t scale AI without a trusted context layer across their data. This round validates what we are seeing at the heart of almost every large business: enterprises are racing to deploy AI, but most are still missing the context layer that makes AI reliable at an enterprise level,
“This funding allows us to move faster, expand strategically across APAC, and help our clients turn trusted data into production-grade AI, not experiments. We are building the foundation that lets AI actually work in the real world,” said Jatin Solanki, Founder and Chief Executive Officer of Decube.
As businesses make significant investments in AI, many are learning the harsh reality that AI systems are only as good as the context they are given.
Even though businesses have sophisticated data platforms, warehouses, and pipelines, they frequently lack a cohesive method to describe the true meaning of their data, its origins, its changes, and whether or not it should be trusted.
According to Decube, the company is developing the context layer for data that many businesses lack.
Practically speaking, it stated that the company provides the crucial context needed for accountable and scalable decision-making by positioning itself between raw data systems and AI or analytics use cases.
Understanding lineage, ownership, quality, and usage policies is part of this—without depending on disjointed tools or manual documentation.
This context layer serves as the cornerstone for business and data leaders, enabling AI projects to confidently transition from experimentation to production.
By combining data understanding into a single system, Decube’s platform assists businesses in moving beyond dispersed metadata, spreadsheets, and tribal knowledge.
Establishing accountability and ownership across data assets, determining the origins and transformations of data, continuously evaluating data reliability prior to consumption, and supplying reliable, explicable inputs to analytics and AI systems are all made possible by this.
