We turn raw potential into real, verified work — mentored internships, AI hackathons, school programs, and published cancer-detection research.
Every student is matched to a live project — not a practice exercise. You work with an experienced mentor, push to GitHub, get PRs reviewed, and ship.
Build a real online store — product catalog, cart, payments, admin dashboard, and deployment.
Train a production-ready ML model, wrap it in a FastAPI service, and deploy it to the cloud.
Design, automate, and monitor a real cloud infrastructure from scratch with CI/CD pipelines.
Go from a real-world problem to a working AI-powered prototype in a single morning event.
From school classrooms to university hackathons to mentored, career-ready internships — the same research-first DNA, a different stage of learning.
Admin places you on a live project with an experienced mentor — no cold applications, no guesswork. Your stream, your level.
Push real commits. Open PRs. Submit weekly logs. Your mentor reviews milestones and signs them off — structured work, week by week.
Complete the final milestone and Praxis issues a certificate with a public verification URL — real proof of real, mentored work.
Three students. Three real prototypes. One morning at the NoCode Crafters Hackathon.
I worked on a real-world problem and built a working prototype — a marketplace for daily-wage workers who have no fixed workplace or wage. Through the app, people can find details of available work nearby.
Going from problem to prototype, it felt like having a mentor working right by my side. I built an AI-powered skill-development platform — "from degree to career" — turning knowledge into employability.
I built SehatSathi — a healthcare access app aligned with SDG 3. Prajnix Studio made the journey from ideation to build genuinely seamless.
Cohort-based live sessions on weekends, plus self-paced tracks with lifetime access.
NumPy, Pandas, visualization — the bedrock of all AI work.
Supervised & unsupervised learning, scikit-learn, evaluation.
Neural networks, CNNs & RNNs with PyTorch.
BERT, GPT, attention, HuggingFace pipelines.
YOLO, segmentation, real-time video analysis.
RAG, LoRA/QLoRA, LangChain, domain LLM deployment.
Pricing on request. Contact us for course fees & schedules →
Deep learning for early laryngeal cancer detection — eight clinically distinct classes from narrow-band imaging. Published in Springer, 2026. Covered by Times of India, Dainik Jagran, and Amar Ujala.
Whether you are a student, an institution, or a clinical partner — PrajniX Labs has a pathway for you.