As AI moves from technological breakthroughs to industrialization, underlying-capabilities are accelerating their convergence. After large models, multimodal models, and agents enter real business operations, data is no longer just a preparation for model training, and computing power is no longer isolated from data and applications. Continuous data supply, model training and inference, agent operation, and stable support in complex hardware and software environments collectively constitute the new foundation for industrial AI moving toward large-scale application.

Recently, KeenData and Tsingmicro completed deep product integration. Relying on their respective technical accumulations in AI data infrastructure and reconfigurable computing, the two parties will further promote the synergy between AI data and computing power, building a more autonomous and stable underlying support for industrial AI.

Computing power provides computational support for model training, inference, and agent operation, while AI data infrastructure continuously undertakes complex data processing, high-quality data supply, and model and agent operation. Smoother collaboration between the two is directly affecting the deployment efficiency and operational stability of AI systems in complex scenarios.

Taking a large energy group as an example, KeenData built a unified AI data foundation centered on a business environment where multiple systems for production and operations run in parallel and data standards are complex. It continuously aggregates and governs cross-system data, transforming it into reusable data supply capabilities, and further supports scenarios such as intelligent questioning, intelligent retrieval, and assisted decision-making.

When AI further enters production environments, it does not rely on any single technology to be accomplished alone. AI data infrastructure needs to adapt jointly with domestic computing power and domestic models: "domestic chips" provide computing support, and "domestic models" carry model capabilities. Only when capabilities at all levels achieve compatibility and collaboration in real business can industrial AI move from single-point capabilities to more complete engineering implementation.

Moving from "being able to compute" to "running intelligence stably" is becoming a more realistic industrial proposition for data-computing synergy.

KeenData continues to improve the adaptation of AI data infrastructure to diverse domestic technology environments, while Tsingmicro has long been deeply engaged in reconfigurable computing technology, forming autonomous computing power capabilities in scenarios such as AI training and inference. This deep product adaptation is an engineering validation of both parties' existing technical capabilities and further enriches KeenData's AI data infrastructure adaptation system for domestic computing power environments.

As computing power continues to evolve, industrial AI's requirements for data infrastructure are also rising rapidly. AI data infrastructure is moving from supporting analytical applications to supporting the entire intelligent production process.

Computing power determines how fast AI can run, while data infrastructure determines how stably AI can run.

As a leader in AI data infrastructure, KeenData's KeenData Agentic Lakehouse Platform intelligent foundation is collaboratively composed of the KeenData Lakehouse multimodal lakehouse platform and the Keen Agentic OS intelligent agent development operating system. KeenData Lakehouse covers multi-source data aggregation, offline and real-time development, multimodal processing, cross-modal alignment, data governance, and high-quality data supply; Keen Agentic OS further addresses model training and inference, Agent Runtime, Multi-Agent collaboration, and enterprise-level governance and security, connecting the key links from data to models and from models to agents.

From complex data processing to high-quality data supply, and from model training and inference to agent development and operation, KeenData is integrating capabilities that were previously dispersed across different technical links into a single intelligent foundation. The deeper industrial AI penetrates into real business, the more AI data infrastructure needs to move from "managing data" to "continuously supporting intelligent operations."

As industrial AI moves toward scale, it is also driving the domestic technology ecosystem from single-point breakthroughs into systematic collaboration. Only when capabilities at all levels—chips, models, and data infrastructure—truly collaborate can artificial intelligence enter production systems more smoothly.

The value of the domestic AI ecosystem does not lie in each being usable individually, but in being usable collaboratively and at scale.

At present, KeenData has continuously advanced engineering practices in core industries such as government affairs, energy, and manufacturing, and related capabilities have been validated in more than 300 large organizations. In terms of ecosystem building, KeenData has continued to advance deep adaptation and compatible co-construction with mainstream domestic computing power chips and domestic large models, continuously improving its ecosystem adaptation capabilities for the domestic technology system. This deep cooperation with Tsingmicro is a part of this continuous expansion of the ecosystem.

Looking to the future, KeenData will continue to take AI data infrastructure as its core, continuously deepen technical collaboration with domestic computing power, domestic models, and industry partners, and drive autonomous technologies from single-point adaptation to systematic co-construction, jointly building a more open, autonomous, and prosperous industrial AI ecosystem.