CoreSmart was not built by academics who theorised a problem. It was built by two people who spent two decades placing technology talent — and watched organisations commission AI courses that changed nothing about how their teams delivered.
Rajiv Khattar and Pranav Sood have run Agama Solutions — a technology staffing and recruitment practice — for over two decades. In that time they have placed thousands of Business Analysts and Software Developers across India's IT services, BFSI, and consulting sectors.
The gap did not appear when ChatGPT launched. It appeared in job requirements. Organisations began asking for AI delivery capability in 2022 and 2023 — and the candidates coming through had certificates, not capability. They could name the tools. They could not deliver with them.
Rajiv and Pranav approached established EdTech providers to explore partnerships. What they found confirmed what they had been seeing in hiring: courses built on theory, assessments built on recall, and graduates who could pass a quiz but could not write an agent requirement, produce a governance pack, or validate AI-generated code in a production environment.
They built CoreSmart to solve a specific, documented, observable problem — not to enter a crowded market. The founding insight: practical use cases and deployed projects solve business problems. Theory and certificates do not.
Rajiv has spent over two decades building and running Agama Solutions — a technology staffing and
recruitment practice placing Business Analysts, Developers, and technical leaders across India's IT
services, BFSI, and consulting sectors.
The experience of watching organisations commission AI upskilling and see no change in
delivery quality is what drove him to build CoreSmart — a programme anchored in what
organisations actually need when they hire AI-capable talent.
Pranav co-built Agama Solutions over two decades, working across technology talent acquisition and
placement for some of India's largest IT services and enterprise organisations.
His vantage point — seeing thousands of job requirements and the candidates who could not
meet them — gave CoreSmart its founding discipline: build the programme from the job
requirement backwards, not from a syllabus forwards. Every CoreSmart deliverable maps to something
an employer actually asks for.
Vinay designed the CoreSmart curriculum and leads both tracks as the primary instructor. His
involvement began when Rajiv and Pranav brought him the hiring data — and his own experience
coaching GenAI adoption at Google confirmed exactly what they were seeing from the talent side.
He is currently VP of AI Strategy and Innovation and an active AI startup advisor.
He teaches from live production experience, not from a course he built three years ago. His Google
credential is publicly verifiable on LinkedIn.
Technology module and all four system design case studies. A CTO who has shipped AI at Audible, Twitch, and Electronic Arts. He teaches what engineering actually needs — from the side that receives the requirements.
Data, cloud, and production AI systems. Currently at Siemens on the same Azure AI stack Indian IT services teams run daily. Ex-Infosys — he knows the delivery model from the inside.
AI strategy, governance frameworks, and business case development. The governance documentation CoreSmart BAs produce in Week 8 is the kind of artefact Deloitte charges consulting day-rates to build.
Output quality evaluation — groundedness, hallucination rates, retrieval accuracy. An ML engineer who measures AI system performance in deployed production environments. Feeds directly into the evaluation modules.
This is not a positioning statement. It is the founding observation that drove Rajiv and Pranav to build CoreSmart rather than partner with an existing EdTech provider.
Every design decision in the curriculum follows from one discipline: what does an organisation actually need when they hire or deploy an AI-capable BA or Developer? The deliverable, the artefact, the thing they can show in a project meeting or a compliance review — that is what CoreSmart produces.
Not awareness. Not a score on a multiple-choice assessment. A named project that proves the capability exists.
CoreSmart exists to close the gap between AI tool adoption and AI delivery capability — for Business Analysts and Developers, in live cohorts, with artefacts that prove it.
Send the AI Readiness Diagnostic to your BA or Developer team. Vinay Bamil reviews every submission personally. A gap report is returned within 24 hours. No call unless you want one.