Artificial intelligence is moving fast, but the infrastructure behind it hasn’t always kept up. As one recent CIO.com analysis put it, “The infrastructure that powers AI today won’t sustain tomorrow’s demands.” For organizations chasing the promise of AI, speed is now the dividing line between falling behind and staying ahead.
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CrateDB, a database company positioning itself as a unified data layer for analytics, search, and AI, believes it has the answer.
Turning Batch Pipelines Into Real-Time Data
Traditional IT systems were built around batch or asynchronous pipelines—great for static reporting, but too slow for the needs of modern AI.
“The challenge is reducing the time between the production and the consumption of the data,” explained Stéphane Castellani, SVP of Marketing at CrateDB. “CrateDB can give you insights into the right data, across large volumes and complex formats, in a matter of milliseconds.”
By acting as a connective tissue between operational data and AI systems, CrateDB supports four stages:
- Ingesting data from multiple sources.
- Aggregating and analyzing it in real-time.
- Feeding insights directly into AI pipelines.
- Creating feedback loops where models continuously learn from new data.
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The payoff is significant. Query times that once took minutes can now be completed in milliseconds—a shift with tangible benefits in sectors like manufacturing, where real-time telemetry supports predictive maintenance and smarter operations.
Beyond Speed: Knowledge Assistance on the Factory Floor
Real-time data isn’t only about efficiency; it’s also about practical decision-making.
Castellani described how manufacturers are using CrateDB as a vector database for knowledge assistance. Imagine a machine flashing an error message an operator doesn’t understand. With a knowledge assistant powered by CrateDB, the worker can instantly access manuals, instructions, and troubleshooting steps—cutting downtime and boosting safety.
Preparing for the Next Wave: Agentic AI and MCP
The pace of AI innovation means no system can afford to stand still.
“We don’t know what AI is going to look like in a few months, or even a few weeks,” Castellani said.
Many organizations are beginning to explore agentic AI workflows, where systems operate with greater autonomy. Yet adoption remains uneven. Research from PYMNTS Intelligence shows the manufacturing sector—despite its potential—lags in deploying these advanced tools.
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To help bridge that gap, CrateDB has partnered with Tech Mahindra, focusing on AI solutions for automotive, manufacturing, and smart factory use cases.
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Another promising development is the Model Context Protocol (MCP), which standardizes how applications provide context to large language models. Castellani compared it to the rise of enterprise APIs a decade ago. CrateDB’s experimental MCP Server aims to act as a bridge between AI tools and analytics databases, laying groundwork for a more connected AI ecosystem.
Looking Ahead
For CrateDB, the strategy remains clear: double down on performance and scalability, expand data ingestion from diverse sources, and continually minimize latency on both ingestion and queries.
As Castellani summed it up: “We keep focusing on our basics.”
In an industry where milliseconds increasingly matter, that focus may prove decisive.
