Power Moodle with Generative AI.

We use Retrieval-Augmented Generation (RAG) to restrict the AI to only answer based on your specific course materials and syllabi, drastically reducing hallucinations. The bot clearly indicates when it cannot find relevant information.

Yes. We use enterprise API endpoints like Azure OpenAI which guarantee that your prompt data is not used to train public models. Private LLM instances can also be deployed on your own infrastructure for maximum data sovereignty.

Yes. We integrate NLP models that evaluate essay submissions based on rubric criteria — assessing relevance, structure, grammar, and conceptual understanding. AI-generated grades can be reviewed by instructors before finalisation.

Yes. We build Moodle blocks that use LLMs to generate multiple-choice questions, true/false quizzes, glossary entries, and lesson summaries from uploaded course materials — saving instructors hours of content creation time.

We integrate OpenAI GPT-4o, Azure OpenAI, Google Gemini, Anthropic Claude, and open-source models like Llama 3 via Ollama. The model choice depends on your budget, data privacy requirements, and performance needs.

Yes. We build predictive analytics plugins that analyse engagement patterns, quiz scores, login frequency, and assignment submission timing to flag at-risk students early — enabling proactive intervention by instructors and administrators.

We index your course resources (PDFs, slides, videos, SCORM packages) into a vector database like Pinecone or pgvector. The chatbot retrieves the most relevant chunks before generating answers, ensuring responses are grounded in your actual content.

An AI chatbot plugin with RAG typically takes 6 to 10 weeks. Automated grading modules take 4 to 8 weeks. We offer phased rollouts starting with a pilot course before full deployment. Costs depend on the AI model, hosting, and feature complexity.