A multi-agent AI tutoring platform that reads your textbooks, answers your questions, generates quizzes, and adapts to your learning style, like having a personal tutor who's read everything.
Students drown in content but starve for understanding. They have access to more textbooks, papers, and resources than ever, but no way to efficiently digest it all. They read a chapter and walk away unsure what they actually absorbed. They highlight entire pages and call it studying.
Meanwhile, faculty can't provide 1-on-1 tutoring at scale, office hours serve a fraction of students who need help. The students who need the most support are often the ones least likely to ask for it.
Traditional study tools, flashcards, summaries, are passive. They don't adapt. They don't know what you're struggling with. And they certainly don't engage you in a conversation about the material.
Students have mountains of reading with no way to extract what matters or verify understanding.
Every student gets the same materials regardless of their learning style, pace, or gaps.
Highlighters and flashcards don't check comprehension or adapt to what you don't know.
Professors can't tutor every student 1-on-1. The students who need the most help ask the least.
Built 7 specialized AI agents (Chat, Research, Solve, Guide, Question, Co-Writer, IdeaGen), each with its own temperature settings, token limits, and prompting strategies. Instead of one general-purpose AI, each agent is tuned for a specific type of learning interaction.
Students upload textbooks, papers, and course materials. The system chunks, embeds, and indexes everything into a searchable vector knowledge base with document tracking and progress monitoring.
Every question is answered using retrieval-augmented generation, pulling relevant passages from the student's own materials, not generic internet content. The Solve agent uses an iterative investigation loop with correction steps for precision.
Integrated with a Common Core standards database (MySQL-backed) so generated questions and assessments map to actual educational standards and competencies.
Let's talk about how an AI-powered tutoring system could work for your institution.
Start a ConversationUpload entire textbooks and papers. Ask any question and get answers grounded in your actual course material, with source citations pointing to specific passages.
Configurable research depth (quick/medium/deep/auto) with subtopic decomposition. Decomposes complex questions into up to 8 sub-investigations across 7 research iterations.
Mastery tracking with adaptive difficulty. The system knows where you're struggling and adjusts questions and explanations accordingly, with three progression levels.
Auto-generates practice questions validated for relevance against your uploaded materials. Maps to learning objectives and educational standards.
Co-Writer agent helps draft essays and papers with real-time editing, inline suggestions, and a Narrator agent with text-to-speech for accessibility.
Transforms concepts into Mermaid diagrams, node graphs (via Cytoscape), and mathematical notation (KaTeX). Supports 8+ languages via i18next.
Upload and manage course materials. See indexing progress, document organization, and knowledge base coverage at a glance.
The Solve agent working through a complex problem with its iterative investigation loop, showing tools used (RAG, web search, code execution) and correction steps.
A deep research session showing subtopic decomposition, source retrieval, and synthesized findings with citations.
The difference between this and other AI study tools is that it actually knows what I'm reading. When I ask a question, it pulls from my textbook, not from some random internet source. It's like having a tutor who's already read the whole syllabus.
A single AI prompt can't be great at tutoring, research, quiz generation, and writing help simultaneously. By splitting responsibilities into 7 focused agents with individually tuned temperatures (Solve at 0.3, IdeaGen at 0.7), each interaction type got dramatically better.
Students forget. A static “you got this right once” mastery score is misleading. The exponential moving average with confidence decay means the system re-tests concepts that haven't been practiced recently, which better reflects actual retention.
The configurable research presets (quick/medium/deep/auto) weren't in the original design. Students asked for them because sometimes you need a quick answer and sometimes you need a 7-iteration deep dive. Giving them that control increased usage significantly.
Tell us about your students' learning challenges and let's explore what an adaptive AI tutor could look like for your courses.
No pitch. No pressure. Just a conversation about what might work.