REZINX
Transparent AI Solutions
AI for Education
We build custom AI systems for EdTech companies and educational institutions, from AI tutors and personalized learning paths to RAG knowledge bases and student support agents. Built with LangChain, LangGraph, and Qdrant.
What We Build
Every system is designed to improve learning outcomes, reduce instructor workload, and scale personalized education.
LangGraph agents that adapt to each student's learning pace, knowledge gaps, and learning style. Asks questions, explains concepts, provides hints, and adjusts difficulty in real time. Provides personalized Socratic learning at scale, available 24/7 for every student.
RAG-powered knowledge base trained on your course materials, textbooks, and lecture notes. Students get instant, cited answers from your actual curriculum, not generic LLM responses. Built with LangChain and Qdrant, it dramatically reduces repetitive instructor questions.
Deep learning models that analyze student performance data, quiz scores, time spent, and error patterns to adapt content difficulty, recommend next topics, and identify knowledge gaps early. Every student gets a learning path optimized for their specific needs.
LangGraph agents that handle student inquiries about enrollment, deadlines, course requirements, and campus resources 24/7. Automates administrative workflows, reminders, notifications, and registration assistance, reducing burden on academic staff.
LLM-powered systems that generate quiz questions, practice problems, explanations, and study guides from your existing course materials. Built with LangChain, it creates varied, curriculum-aligned content at scale, reducing instructor content creation time significantly.
AI-powered analytics that surface insights from student performance data, identifying at-risk students early, tracking cohort progress, and predicting outcomes. Gives instructors actionable intelligence to intervene before students fall behind.
We build Socratic AI tutors using LangGraph state machines. Instead of simply generating answers, the model is guided by structured rules to ask probing questions, give small hints, identify the student's misconceptions, and encourage them to arrive at the solution independently.
Yes. Using Retrieval-Augmented Generation (RAG) with LangChain and Qdrant, the AI is grounded strictly in your provided textbooks, lectures, and guides. The model is explicitly instructed to refuse to answer questions outside the scope of your curriculum to prevent cheating or off-topic queries.
We implement FERPA-ready and GDPR-compliant architectures. Student data is processed in secure, isolated sandboxes. We scrub personally identifiable information (PII) before it is sent to any external model APIs and utilize local vector search indexing to prevent exposure.
Absolutely. We build LTI-compliant integrations that connect directly to major Learning Management Systems (LMS) like Canvas, Moodle, and Blackboard, allowing students to access AI tools directly from their course portals.
By handling up to 80% of repetitive questions regarding syllabus guidelines, grading policies, and standard course concepts. TAs can review query history dashboards and focus their attention on complex student requests.
We use model orchestration to dynamically choose the right tool: lighter models (like GPT-4o-mini or Claude Haiku) for quick Q&A to keep latency low, and advanced reasoning models (like Claude 3.5 Sonnet) for custom feedback and tutoring.
Ready to scale your learning platform? Book a free consultation with our AI software engineers to design your educational agents and RAG integrations.
Book a Free EdTech Consultation