Agentic AI research platform for scientists and PhD researchers
We built DARE — an agentic AI platform that lets researchers without engineering backgrounds build, run, and share sophisticated AI workflows on their own infrastructure. Now deployed at Carnegie Mellon University.
Modern research teams increasingly rely on AI to accelerate discovery, but building and managing advanced AI workflows often requires specialized engineering expertise. Many organizations also face strict data governance requirements that limit the use of third-party AI platforms. The objective was to create an AI-powered research environment that enables scientists and analysts to leverage intelligent automation while maintaining full control over their data and infrastructure.
AI has the potential to transform scientific research, but the tools to harness it are designed for engineers, not researchers. Scientists who want to use AI agents to process massive datasets, run complex analysis workflows, or fine-tune models on proprietary research data face steep technical barriers. Researchers without dedicated engineering support could not build custom AI agents their work required. Using commercial AI platforms required sending sensitive research data to external servers, violating many institutions' data policies.
We designed and developed DARE, an agentic AI platform tailored for research and knowledge-intensive workflows. The platform provides an intuitive interface for configuring AI-driven processes that can analyze large datasets, generate summaries, extract insights, and orchestrate multi-step analytical tasks without requiring extensive programming expertise. Designed with deployment flexibility in mind, the solution supports customer-controlled environments and integrates with existing AI and knowledge management ecosystems.
The platform was successfully deployed at Carnegie Mellon University and is now accelerating AI-powered research across multiple disciplines. Researchers without engineering backgrounds can now build sophisticated AI agents for their specific research needs. All processing happens within institutional infrastructure, keeping sensitive research data protected. Automated AI workflows significantly reduced manual research effort, and research workflows can be saved, shared, and replicated across teams.
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