Proven at Scale
Gyanis AI powers the Gyanis learning platform — used by students across India, UAE, and Somalia preparing for JEE, NEET, UPSC, CBSE, and international exams.
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Taxonomy Endpoints
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API Key Scopes
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Webhook Event Types
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Model Tiers
Education-Specific Fine-Tuning
Not a generic model with an education prompt. A model that understands education.
Gyanis AI models are fine-tuned on real educational interactions spanning JEE, NEET, UPSC, CBSE, ICSE, IGCSE and IB — their curricula and their exam formats. The result is an AI that understands pedagogical context, explains concepts at the right level, and knows when to guide versus when to give answers.
Retrieval-Augmented Generation (RAG)
Every answer grounded in verified educational content — not just the model's training data.
RAG combines the fluency of large language models with the accuracy of a curated knowledge base. When a student asks a question, the system retrieves relevant educational content from verified sources before generating a response — eliminating hallucination and ensuring curriculum alignment.
Curriculum-Aligned by Construction
Retrieval decides what a response is grounded in. The curriculum taxonomy decides where that content sits. Gyanis maintains a canonical Board > Grade > Subject > Unit > Topic model of the syllabus, and publishes it as the read-only Gyanis Taxonomy API — so your application can read a topic’s exact address and pass it into a chat request.
Topic context fragments
GET /taxonomy/topics/:topicId/context returns an LLM-ready block for one topic — canonical path, learning objectives, prerequisites, keywords, difficulty, sample prompts and tags. That block is what turns a generic prompt into a syllabus-bounded one.
Exam-track weightage
Exam tracks describe a competitive exam’s format — sections, marks, negative marking and sittings — and carry per-topic weightage mapped onto curriculum topics, so a study plan can allocate time by what the exam actually weights.
Maintained freshness
Syllabi change, and Gyanis maintains them. GET /taxonomy/changes serves cursor-paginated deltas with delete tombstones over a rolling 180-day window, and the taxonomy.updated webhook tells you when there is something new to poll for.
How does Gyanis keep AI answers inside the syllabus?
Gyanis grounds every response twice. Retrieval pulls verified curriculum content, and the Gyanis Taxonomy API supplies the exact syllabus address — a canonical path such as CBSE > Class 10 > Physics > Optics > Refraction of Light, plus learning objectives and prerequisites — which your application passes into the chat request as system context.
What happens when a board changes its syllabus?
Gyanis maintains the curriculum taxonomy and publishes the revision through the Gyanis Taxonomy API — no model retraining is involved, because the structure is data rather than weights. Applications that mirror the structure locally subscribe to the taxonomy.updated webhook and then call GET /taxonomy/changes, which returns cursor-paginated deltas and delete tombstones over a rolling 180-day window.
Multi-Layer Content Safety Pipeline
Four layers of protection between the AI and your students.
Every interaction passes through a multi-stage safety pipeline designed specifically for educational environments. Safety is not a feature flag — it is architecturally embedded into every API call.
Input Screening
Student queries are screened for inappropriate content, prompt injection attempts, and off-topic requests before reaching the model.
Context-Aware Filtering
The model operates within educational guardrails. It will not provide harmful information, generate inappropriate content, or deviate from the educational context.
Output Moderation
Every AI-generated response is automatically moderated before delivery. Flagged content is held and triggers webhook notifications to your system.
Continuous Monitoring
All interactions are logged with full audit trails. Moderation analytics help identify patterns and improve safety thresholds over time.
Gyanis AI vs Raw API Providers
Using generic AI APIs directly for education means building everything from scratch. Gyanis AI gives you a complete education-ready AI stack.
Technology Questions
Common questions about our AI technology, fine-tuning, and safety pipeline.
Our models are trained on real educational interactions spanning JEE, NEET, UPSC, CBSE, ICSE, IGCSE, and IB curricula. This fine-tuning teaches the model pedagogical patterns — how to explain concepts step-by-step, when to ask clarifying questions, and how to adapt explanations to different student levels.
Retrieval-Augmented Generation retrieves relevant educational content from a curated knowledge base before generating each response. This grounds answers in verified material rather than relying solely on the model's training data, dramatically reducing hallucination and ensuring curriculum-aligned accuracy.
The multi-layer safety pipeline runs automatically on every interaction. You can configure safety thresholds per API key and receive webhook notifications when content is flagged. The pipeline includes input screening, context-aware filtering, output moderation, and continuous monitoring.
Yes. While our models are specifically fine-tuned for JEE, NEET, UPSC, CBSE, ICSE, IGCSE, and IB, the underlying educational AI capabilities work across any curriculum. Contact us to discuss fine-tuning for your specific educational context.
The input screening layer detects and blocks prompt injection attempts before they reach the model. Combined with context-aware filtering that enforces educational guardrails, the system prevents students from manipulating the AI into generating off-topic or inappropriate content.
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