For Chris, every conversation about technology eventually circles back to the same point: great teaching is about knowing students deeply. Data isn’t valuable because it looks nice in a chart; it’s valuable because it helps teachers understand the quiet kids, the bubble kids, the ones who get overlooked, and the ones who need a different analogy to make a lesson click. AI’s role in all of this isn’t to replace teachers' instincts but to surface the information that makes meaningful connections possible faster. When educators can see patterns clearly and in plain English, they can spend more time engaging students in ways that feel personal.
The reality is, not every teacher is a tech expert, and not every educator wants to decipher pivot tables. Otus was built with that truth in mind. By organizing data programmatically, presenting it clearly, and letting teachers ask natural-language questions like “How are my third-graders with IEPs doing?”, the platform turns what would normally be overwhelming student information into something instantly usable. The real result isn’t flashy reports, either. It’s confidence. Teachers can group students, identify needs, and double-check their instincts without digging through systems or worrying whether they’re reading the data right.
Chris makes it clear: AI can accelerate planning, suggest targeted strategies, and surface insights that would’ve taken hours to arrive at, but it should never operate unsupervised in K-12. Otus intentionally delivers AI outputs to educators, not directly to students, ensuring a guided, thoughtful workflow. Teachers remain the human-in-the-loop, choosing which strategies to use, validating insights, and making the real instructional decisions. In other words, AI shouldn’t make teaching easy; it should make the prep easier so teachers can focus on creating the conditions for deeper learning, better grouping, and more personalized support.