Data Engineering and Custom Solutions

To build AI that truly understands NAU, we need to connect it to our institutional knowledge. Our team works with departments across the university to create data-driven solutions that are secure, accurate, and aligned with our mission. We focus on building the “digital plumbing” that allows AI to help us make better decisions and serve our students more effectively.

Our Collaboration Areas

We use Microsoft Fabric as our unified data platform. It allows us to bring information from different systems together in one secure location. If your department has complex data needs, we can help you set up a workspace within Fabric. This ensures your data is organized, accessible, and ready to power advanced analytics or AI agents.

Accessing our core university data (ERP systems) is essential for building helpful AI, but it must be done with the highest security standards. We partner with you to create secure pipelines that allow AI agents to “read” relevant data without ever compromising privacy or institutional integrity.

Beyond general-purpose chat, we help you build specialized agents designed for specific tasks. These agents are “grounded” in your department’s unique data, meaning they provide answers based on NAU policies and records rather than general internet knowledge. This is the difference between an AI that knows about higher education and an AI that knows how your department operates.

Not sure if your data is ready for AI? We provide “readiness” consultations to look at how your information is currently stored. We help you clean, organize, and structure your data so that when you are ready to deploy an AI solution, it performs reliably from day one.

Successful AI projects are about more than just the software. They require finding the right match between a specific operational challenge and the platform best suited to solve it.

We start every engagement with a collaborative discovery session to determine whether your goals are best met by a simple Copilot or a complex, custom-coded solution in Azure. Once we have identified the right path forward, we follow a structured partnership process to move your idea safely from a concept to a secure, reliable tool that supports your daily work.

Working with institutional data is a shared responsibility. When you engage with our team, we follow a structured process to ensure success:

  1. Discovery: We meet to understand your goals and the specific “pain points” you want to solve.

  2. Feasibility & Security: We evaluate the data sources and work to ensure the project meets all NAU privacy standards.

  3. Prototype: We build a small-scale version of the agent or data pipeline to prove the concept.

  4. Scaling: Once validated, we help you move the solution into your daily operations.

We don’t believe in a one size fits all approach. Every project begins with a discovery session where we look at your goals and technical requirements. Our team will walk through the options with you to determine the best fit:

  • Basic Agents: Best for simple, single-task needs with minimal data requirements.

  • Copilot Studio: Ideal for rapid, low-code solutions that live within the Microsoft 365 ecosystem.

  • Azure AI Foundry: The choice for high-scale, custom-coded applications that require complex logic or unique integrations.

Sustainability: Engineering for 2030

Data storage and processing have a physical footprint. As part of our commitment to reaching carbon neutrality by 2030, we leverage advanced features within Microsoft Fabric to minimize our environmental impact:

  • OneLake Shortcuts: We use “shortcuts” to live-link to data where it already lives (like Azure or AWS) instead of creating redundant copies. This reduces unnecessary storage and the energy required to move massive datasets.

  • Mirroring: This allows us to keep our analytical systems in sync with our operational databases in near real-time without complex, energy-heavy ETL (Extract, Transform, Load) processes.

  • Lean Architecture: By maintaining a “single version of truth” through these features, we significantly reduce data sprawl. We only process what is necessary, ensuring our technological growth stays in balance with our climate goals.

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