AI Academic & Professional Training

Applied AI, taught by a
real-world practitioner,
on a real-life domain.

Courses, certifications, and campus programs in real-world AI — for institutions, professionals, and students.
Hands-on, drawn from practice — not theory from the sidelines.

The programs

Four variations, Choose depending on your need.

Academic Certifications Institution-credentialed, examined certifications for working finance professionals.

Structured certifications delivered through an academic institution — credentialing, scheduling, and examination sit with the host. Practitioner-designed content, institutional weight behind the certificate.

IMT-credentialed

AEFM Level 1

AI-Enabled Financial Markets · One Saturday · five sessions · online · MCQ exam · certificate + badge. For analysts, advisors, RIAs, and capital-markets professionals.

Explore AEFM →
In development

AEFM Level 2

Advanced, deeper specialisation that builds on the Level 1 foundation. Details to follow.

Campus Programs (Finance) Brought into MBA / PGDM programs as visiting faculty. Whole-of-finance, judgment-led.

A complete, certified applied-AI module delivered on your campus in a single engagement — no recurring scheduling, a co-brandable certificate, and a project viva that makes the credential defensible. Built to make your finance students AI-native before they walk into a BFSI interview.

Visiting faculty

Applied AI in Finance

GenAI → RAG → Agentic AI core, with two ready domain lanes — Investments & Wealth and Trading & Capital Markets. EGA judgment layer, hands-on labs, and a project viva that gates a co-brandable certificate.

Explore the module →
For deans & directors

Why it lands

One visit, complete curriculum, real credential. Flat institutional fee with no cap on student numbers. Flexes to your timetable — block or weekly. Placement-relevant: students can defend an AI workflow.

Download the brief →
Campus Programs (Tech) For engineering, BCA, MCA & IT students. Build and ship working AI applications.

The same spine — generative AI, RAG, and agents — aimed at students who will build these systems, not just use them. Less domain theory, more engineering: how LLM applications are architected, grounded, integrated, and shipped responsibly. A practical alternative to another theory-heavy ML course.

Visiting faculty

Applied AI Engineering

LLM apps & prompt engineering, RAG systems (retrieval, grounding, evaluation), agentic workflows & tool-use (MCP, skills), and shipping responsibly — observability, guardrails, evals.

Explore the program →
For HODs & placement

Why it lands

Job-ready GenAI skills industry is hiring for now. Project-based — students ship a working app. Pairs naturally with existing CS/IT curricula. Build-first: they demo & defend, not submit slides.

See what students build →
Professional Workshops Coming soon Open-enrolment, practitioner-led workshops for traders, sub-brokers & RIAs.

Hands-on workshops individuals enrol in directly. Real prompts, real charts, working tools you keep — built for people active in the markets right now.

3-part series

AI in Trading & Investing

Part 1 · Technical Analysis. Part 2 · Futures & Options. Part 3 · Fundamental Analysis. For active traders, sub-brokers, and aspiring RIAs.

See the series →
Format

How it runs

Online · live · instructor-led. Short, focused sessions, with a working tool you take away. Inaugural cohorts opening soon.

The common DNA

Different audiences. Same standard.

Whatever the room, every program is built on the same four commitments.

Method

Hands-on, not lecture-only.

  • Theory, guided demos, DIY demos, take-home work
  • Students operate real workflows, not slides about them
  • RCFOC prompting discipline applied throughout
  • You leave with something working, not just notes
Judgment

Built on EGA, always.

  • Evidence, Governance, Accountability — the through-line
  • Knowing when to trust AI and when to override it
  • Grounded in Indian regulatory reality (SEBI / RBI / DPDP)
  • AI applied with judgment, not automated without it

Why a practitioner, not a trainer. These programs are taught by someone who builds and governs AI systems for a living — in a regulated industry, with real consequence attached. The content comes from operating the work, not from reading about it. Specialist co-faculty join for specific domain lanes where deeper subject expertise adds value.