CASE STUDY to characterise and observe in actual production systems. These inputs are not readily available to our researchers at CMU.
This partnership bridges this gap for research on data life-cycle management topics, ensuring that advanced methods developed at CMU for these topics are not only theoretically sound but also applicable to realworld deployments.
Rik Tamm-Daniels
In what way is the partnership with Carnegie Mellon‘ pushing the boundaries’ of what’ s possible with GenAI in data management?
Our partnership with Carnegie Mellon combines the exploratory nature of academic research with the real and evolving commercial data needs of modern enterprises. This collaboration gives us a unique opportunity to explore long-term, visionary topics that are critical to shaping the future of data management and Generative AI.
Ultimately, the partnership enables us to contribute beyond our commercial objectives. It creates a path to support fundamental research that benefits the broader AI and technology community, helping advance the field forward together.
How will Informatica go about bringing the transformative benefits of the collaboration to customers and the industry at large?
Any insights or breakthroughs that come out of the collaboration will naturally be shared with the wider community, as is typical in academic research. At the same time, where we uncover results with clear commercial relevance, we’ ll look to align those outcomes with our product development efforts, augmenting and improving our offerings to incorporate innovations born out of the partnership. For example, these advancements can benefit our own AI technology including our CLAIRE GPT, CLAIRE AI Copilot and recently announced AI Agent Engineering offerings, by bringing transformative capabilities to our customers and the industry at large.
Collaborating with Carnegie Mellon, known for its academic rigor and world-class reputation – particularly in computer science – opens doors to research opportunities that push the boundaries of what’ s currently possible with Generative AI and data management.
As part of this work, we’ re jointly developing a common research agenda that covers a wide spectrum of topics, from hardware-level optimization to the operationalization of GenAI at enterprise scale. We’ re also investigating areas like Agentic AI and how a strong foundation of metadata intelligence can dramatically improve the agent experience in large, complex environments.
Our aim is to create a differentiated, industry-leading, AI-led experience for our customers – powered by the learnings from the collaboration. We’ re equally committed to contributing back to the broader research and technology community. So, while we’ ll apply what makes sense to our product roadmap, much of the knowledge generated will also be shared openly through Carnegie Mellon and its research channels.
Overall, we’ re hoping to contribute key innovations to the wider AI research community, reinforcing our position as a trailblazer in Generative AI. Working side-by-side with Carnegie Mellon’ s faculty and staff, we’ re helping drive the next wave of meaningful AI advancements and solve real-world challenges at scale. p
Carnegie Mellon University, Pittsburgh
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