Enterprise AI adoption is increasingly exposing an old infrastructure problem: companies may have plenty of data, but much of it remains fragmented, difficult to query and poorly prepared for AI systems. Singdata is positioning itself to address that gap after becoming a founding ecosystem partner of Geene 2.0, a new enterprise AI commerce platform from Synagie’s parent company, Hong Kong-listed Synagistics Limited.
Scheduled for commercial launch on August 8, 2026, Geene 2.0 brings together 12 founding partners across AI, data, content, commerce, entertainment and verification. Singdata will provide the platform’s data infrastructure, focusing on business intelligence, financial-process automation and market-intelligence workflows.
The enterprise AI bottleneck is moving underneath the model
The current enterprise AI race is often framed around increasingly capable large language models and AI agents. But for many companies, the harder problem sits further down the stack.
Data is scattered across ERP systems, spreadsheets, commerce platforms, financial applications, logistics systems and customer databases. Making that information consistently available to AI—while preserving governance and control—is a different engineering problem from selecting a model.
That is the problem Singdata says it is addressing through its partnership with Geene 2.0, described by Synagie as a “trusted AI commerce” ecosystem.
The platform divides enterprise AI-commerce workflows into five layers: analysis, strategy, content, execution and trusted verification. Each layer is assigned to specialized ecosystem partners rather than requiring enterprises to assemble every capability themselves.
Singdata is responsible for the data layer.
The 12 founding partners include Synagie, Singdata, BytePlus, China Mobile International, FLY Entertainment, Mei Ah Entertainment, DataCloud, Yiwise, Zeneva, DU-MAS, International Brand Centre and Masverse.
That architecture reflects a broader shift in enterprise AI. Instead of treating AI as a standalone application, vendors are increasingly building stacks that connect models to governed enterprise data, business processes and external systems.
Singdata’s pitch: make data usable without rebuilding the kitchen
Singdata’s role in Geene 2.0 centers on three recurring enterprise tasks: automated business-intelligence reporting, financial-process automation and market-intelligence collection.
The company says its platform allows operations teams to use natural-language requests to surface information such as sales trends and inventory movements rather than manually assembling reports.
Singdata Co-founder and CEO Ethan Yu says the company’s tools are designed to take repetitive reporting and reconciliation work away from operations teams. The company claims that around 90% of such repetitive work can be delegated to AI in relevant workflows.
That figure is a vendor claim rather than an independently verified productivity benchmark.
The underlying idea is more significant than the number. Enterprise AI cannot reliably automate a workflow if the information required by that workflow is inaccessible, stale or inconsistent.
Singdata’s answer is its Lakehouse architecture, powered by its self-developed Generic Incremental Computation (GIC) engine. According to Singdata’s technical documentation, GIC processes only data changes rather than repeatedly recomputing entire datasets. It is designed to incrementally execute standard SQL operations, including more complex workloads involving joins and updates.
That approach matters because real-time enterprise analytics can become expensive when every update triggers a full recalculation.
Why AI-ready data is becoming its own market
The Singdata-Geene 2.0 partnership arrives as enterprise technology buyers increasingly recognize that AI adoption depends on the quality and accessibility of the underlying data.
Gartner said in 2026 that more than 75% of organizations are prioritizing investment in AI-ready data, while describing data readiness as a major barrier to scaling AI.
Gartner also previously found that 63% of organizations either lacked or were unsure whether they had appropriate data-management practices for AI, and predicted that organizations would abandon 60% of AI projects through 2026 when those projects lack AI-ready data.
IDC similarly estimates that global IT spending on AI will reach $409 billion in 2026, up approximately 53% year over year. Yet IDC says many organizations remain stuck in targeted deployments rather than broad operationalization, with poor data foundations among the factors limiting returns.
The implication is straightforward: the enterprise AI market is creating demand not only for models and agents, but for the infrastructure that connects those systems to trustworthy business information.
That puts companies such as Singdata into a crowded but increasingly important category alongside data warehouses, lakehouses, integration platforms, semantic layers and AI-ready data-management systems.
Competing with the broader data-stack ecosystem
Singdata is not competing directly with a single AI model provider.
Its position sits closer to the data infrastructure layer occupied by technologies from companies such as Snowflake, Databricks and cloud providers including Amazon Web Services, Google Cloud and Microsoft Azure.
The difference is architectural emphasis.
Traditional data platforms generally give enterprises the tools to store, process, govern and analyze information. AI-native data infrastructure increasingly has to make that information usable by machine reasoning systems, AI agents and multimodal applications.
Singdata says its Lakehouse operates across seven cloud platforms, including AWS, Google Cloud and Alibaba Cloud. The company also says it is a core contributor to Apache Iceberg, an open table format for data lakes.
For enterprise buyers, multi-cloud support and open data formats can reduce the risk of locking critical business information into one infrastructure provider.
That consideration is becoming more important as AI workloads become increasingly expensive and cloud infrastructure spending accelerates. Global cloud infrastructure spending reached $143 billion in the second quarter of 2026, according to Synergy Research Group data reported by ITPro, with generative AI a major growth driver.
Geene 2.0 is betting on an ecosystem, not a single AI product
The broader Geene 2.0 model is notable because it distributes responsibilities across partners.
Rather than presenting an all-in-one AI product that handles models, content, commerce execution and verification internally, the platform attempts to coordinate specialists.
That resembles the direction enterprise software has taken elsewhere. Salesforce, Microsoft and Google increasingly combine AI capabilities with partner ecosystems, application marketplaces and data platforms rather than relying exclusively on internally developed functionality.
For Geene 2.0, the challenge will be orchestration.
An ecosystem model only works if data can move reliably between layers, permissions remain enforceable, outputs can be audited and each partner’s technology behaves predictably within the wider workflow.
Singdata’s “trusted data” positioning therefore becomes central to the proposition. If the data layer is inconsistent, every downstream AI output can inherit the problem.
What enterprise teams should watch
For CIOs, chief data officers and enterprise AI teams, the Singdata partnership reinforces several practical priorities.
First, AI readiness should be assessed at the data layer before organizations scale agents or automated decision-making. Second, enterprises need clear ownership of the data being exposed to AI systems. Third, open formats and multi-cloud compatibility can become strategic considerations as AI infrastructure costs rise.
The technology also points toward a future in which business users increasingly ask questions of operational data in natural language rather than navigate dashboards and predefined reports.
But natural-language access does not eliminate the need for data governance. It makes governance more important because the system is potentially translating business questions directly into queries, analysis and actions.
That is why the real test for Geene 2.0 will not be whether it can generate a report in minutes. It will be whether enterprises can trust the underlying data, reproduce the result, control access and integrate the output into real business workflows.
Singdata’s role suggests that the next phase of enterprise AI may be won less by whoever has the most impressive model and more by whoever can make fragmented corporate data reliably usable by that model.
Market Landscape
Enterprise AI is moving from experimentation toward operational deployment, but data readiness remains a major constraint. Gartner says AI-ready data investment is becoming a priority for more than 75% of organizations, while IDC projects $409 billion in global AI IT spending during 2026.
The competitive landscape includes cloud providers such as AWS, Google Cloud and Microsoft Azure, data-platform vendors including Snowflake and Databricks, open data technologies such as Apache Iceberg, and an expanding layer of AI-native data and agent infrastructure.
The strategic opportunity for companies such as Singdata is to sit between existing enterprise data systems and the AI applications increasingly being deployed on top of them.
Top Insights
- Singdata is joining Geene 2.0 as the ecosystem’s data-layer partner, supporting business intelligence, financial automation and market-intelligence workflows.
- Geene 2.0 uses a multi-partner architecture, dividing enterprise AI commerce into analysis, strategy, content, execution and trusted-verification layers.
- AI-ready data is becoming a strategic priority, with Gartner identifying data readiness as a major barrier to enterprise AI scale.
- Singdata’s Generic Incremental Computation engine is designed to process changing data efficiently, supporting more responsive enterprise analytics and AI workloads.
- Enterprise buyers increasingly need infrastructure beneath AI agents, including governed data, interoperability, real-time processing and multi-cloud deployment options.
Get in touch with our fintech expert
