Your syllabus. Industry's hiring bar. We build the bridge on your campus.
CoreSmart Campus starts by mapping what your students already study against what recruiters are asking for right now, then fills the gap stream by stream. Our trainers deliver in person on your campus. Every student builds and submits projects that we review. And everything taught in the classroom stays available to your students and faculty on the CoreSmart LMS.
Where a typical AI syllabus ends, and where hiring begins.
Most degree programs give students strong foundations. Recruiters now test for things that were not in the syllabus when it was approved. The map below is our starting point with every department: we replace the middle column with your actual syllabus in a mapping workshop, and the right column becomes your program.
| Topic | Typical university syllabus | What industry is asking for now | What CoreSmart adds |
|---|---|---|---|
| Programming and mathematics | Covered Python, data structures, algorithms, discrete maths, probability and statistics. | Working fluency in Python (including async), Git, APIs, and reading production code. | A short bridge module for students who need it. No repetition of theory. |
| Data engineering and databases | Partly DBMS, data warehousing, big data analytics, stream processing as electives. | Building pipelines that feed AI systems: SQL, document ingestion, chunking, embeddings, vector stores, data quality. | Taught as part of the RAG and agent builds, on real, messy documents. |
| Classical machine learning | Covered Regression, classification, clustering, model evaluation. | Knowing when a classical model beats an LLM, and how to combine them. | Applied through projects, not re-taught. |
| Deep learning, NLP, computer vision | Covered Neural networks, CNNs, RNNs, transformers in theory; text, speech, image analytics as electives. | Using pretrained and foundation models rather than training from scratch. | Bridges theory to foundation-model practice at the start of the program. |
| Generative AI and LLM applications | Missing Appears as one topic in the final module of the AI course (GPT, BERT, diffusion), not as a course. | Building LLM-powered applications with structured outputs, tool calling, and streaming. Asked in nearly every AI-track interview. | Full module: students ship a working LLM application early in the program. |
| Prompt and context engineering | Missing Not in the syllabus. | System prompts as software contracts, context management, long-context and memory strategies. | Taught in the first build and reinforced in every project rubric. |
| Retrieval-augmented generation | Missing Rarely in the syllabus. | Embeddings, vector search, grounding, and hardening RAG for accuracy. A standard architecture in enterprise. | Full module with a RAG system over real documents. |
| AI agents, tool use, multi-agent systems | Missing Not in most curricula yet. | Agents with tools, memory, planning, orchestration, and tool protocols such as MCP. The fastest-growing area in AI hiring. | Two modules: single agents, then multi-agent orchestration with guardrails. |
| Fine-tuning and open-weight models | Missing Transfer learning and fine-tuning of CNNs appear in deep learning electives; adapting language models does not. | When to fine-tune versus RAG versus prompt, parameter-efficient tuning, serving open-weight models. | One build comparing a fine-tuned small model against RAG on the same task. |
| Evaluation and reliability | Missing Accuracy metrics only. | Test sets, hallucination control, LLM-as-judge, regression suites, judging output quality. The difference between a demo and a system. | Full module. Every project is evaluated on these criteria. |
| AI security | Missing Cryptography and network security are core; electives apply ML to intrusion detection. Nothing covers securing AI systems themselves. | Prompt injection, jailbreaks, data leakage through tools, guardrails, red-teaming, the OWASP Top 10 for LLM applications. Now a compliance requirement in enterprise. | A dedicated module: students red-team their own application and ship it with guardrails and a security report. |
| Responsible AI, governance, and privacy | Partly Ethics and AI elective, human values course; mostly theoretical. | Bias, PII handling under the DPDP Act, audit trails, knowing when not to trust AI output. | Built into the security and evaluation modules and the project rubric. |
| Deployment, LLMOps, and cost | Partly "Model deployment concepts" as one topic; cloud and DevOps as electives; nothing on serving LLM systems. | Shipping, logging and tracing, monitoring, caching and model routing, token cost control, keeping an AI system running. | Every student deploys their capstone with tracing, monitoring, and a cost report. |
| Multimodal and voice | Missing Speech and image analytics exist as separate electives, not combined with LLMs. | Document AI, vision-language models, speech agents in Indian languages for customer and citizen services. | Offered as capstone tracks where the stream and placement partners call for it. |
| Domain application | Missing AI taught separately from the student's own field. | AI applied to the recruiter's domain: manufacturing, infrastructure, telecom, security, finance, operations. | Capstones scoped per stream and to your placement partners' domains. |
| Portfolio and interview readiness | Missing Left to the student. | A public project a recruiter can open, and the ability to explain it under questioning. | Verified project record, public repository, and a placement interview bootcamp. |
The "typical syllabus" column is drawn from current Indian regulations, including Anna University R2021 and autonomous R2024 curricula for CSE, AI&ML, and AI&DS, with their professional elective verticals and industry-connected elective baskets. Your department's mapping replaces it with your actual course structure before anything is delivered.
One program shape, mapped separately for each stream in your university.
AI hiring is not limited to computer science. Each stream has its own starting point, its own recruiters, and its own gap. We run the same structure for each, with the content, tooling depth, and capstone changed to fit. Engineering streams build in code; business streams build with no-code and low-code tools and focus on directing and judging AI.
CSE / IT / AIML / Data Science
ECE / EEE / Embedded
Mechanical / Production / Civil
BBA / MBA / Commerce / Economics
Other streams (biotech, pharma, architecture, law, media) are mapped the same way on request. If your university has a stream not listed here, the mapping workshop covers it.
Projects that look like the work, not like homework.
Every project below is a system a recruiter would recognise from their own backlog: it takes real data, has to be evaluated, has to be secured, and has to run. These are illustrative. The final list for your cohort is scoped in the mapping workshop and tied to your placement partners' domains.
Campus Copilot
A RAG assistant over the university's own regulations, syllabus, circulars, and exam rules. Answers with citations, refuses when the answer is not in the documents, and is scored on a test set of real student questions.
Break Your Own Bot
Students build a customer-support agent for a mock bank, then red-team it: prompt injection through uploaded documents, data exfiltration through tools, jailbreaks. They ship it again with guardrails and an OWASP-style security report.
Fine-tune or Retrieve?
The same classification task solved three ways: prompting, RAG, and a fine-tuned small open-weight model. Students measure accuracy, latency, and cost per thousand requests and write the recommendation an engineering manager would expect.
Network Incident Triage Agents
A multi-agent system that reads alarm and log streams from a simulated telecom network, groups related faults, drafts the incident ticket, and escalates by severity. Evaluated against a labelled incident history.
Edge Sensor Watchdog
Anomaly detection on live sensor data running on a low-cost board, with a small language model that explains the fault in plain language and a cloud agent that decides whether to alert. Students measure what runs on the device and what must not.
Predictive Maintenance to Work Order
A maintenance model on machine telemetry feeding an agent that drafts work orders, checks spare-part availability, and schedules the job. Scoped to a manufacturing partner's equipment where one exists.
Site Planning Assistant
An assistant over project schedules, drawings, and BIM exports that answers "what is delayed and why", flags clashes, and drafts the weekly progress report. Evaluated by a site engineer's checklist.
Invoice and Contract Extraction Pipeline
A document pipeline that pulls fields from scanned invoices and contracts, validates them against rules, routes exceptions to a human, and reports its own error rate. Built with low-code tools by business streams and in code by engineering streams.
Citizen Services Voice Agent
A voice assistant in Tamil, Hindi, or another Indian language that explains a government scheme, checks eligibility, and books an appointment, with a written evaluation of where it misheard, misled, or should have handed off to a person.
Every project is submitted through the LMS, reviewed against the published rubric, deployed, and presented at Demo Day. Capstones for your cohort are scoped to the domains of your key placement partners so the demo is relevant to the people who hire from your campus. Where your regulation allows, the capstone can be registered as the student's Project Work or prototype development lab, so it earns credit instead of competing with it.
Our trainers come to your campus and teach in person. Your faculty stay in the loop, not in the workload.
Online-only programs do not hold a campus cohort to completion, and we do not ask your faculty to teach a field that changes every few months. CoreSmart trainers deliver the sessions in your labs, on weekdays, inside your timetable. Faculty receive an orientation so they understand what is being taught and can follow the cohort on the LMS.
Mapping workshop
We sit with the department, go through the current syllabus for each stream, and agree what the program will add. Capstones are scoped to your placement partners' domains here.
Timetable and cohort
Weekday slots, lab access, and sections of 60 to 120 students. Larger batches run as parallel sections with a trainer plus teaching assistants each.
In-person delivery
A CoreSmart trainer teaches every session on campus. Each session is build-first: students leave with something working, not with notes.
Demo Day and report
Students present deployed capstones to a panel. The department and placement cell receive an outcomes report and the full project record.
Your faculty understand the program without having to run it.
A short, certified orientation before the cohort starts covers the mapped curriculum, the projects, and the rubric. Faculty get LMS access to every recording and lab, so they can follow the cohort, support students between sessions, and see exactly what was taught.
- Orientation on content, projects, and grading before kickoff
- Full LMS access for faculty throughout and after the program
- Faculty-delivered model available later, priced separately, once the first cohort has run
No lab upgrade. No student paying for API access.
Students work in the CoreSmart lab environment with model access credits provided by us. The program runs on your existing machines and network.
- Hosted lab environment and model access credits per student
- Open-weight models used wherever they fit, to keep costs and dependencies low
- Datasets and starter code provided for every lab and project
Every student builds, submits, and gets reviewed. Everything taught stays available online.
Two things separate a program that produces hireable graduates from one that produces attendance: whether each student's work is actually reviewed, and whether the material is still there when they need it before an interview. Both are built into how we run.
A build every module, submitted on the LMS, reviewed by practitioners.
Every module ends with a build that students submit through the LMS. Each submission is reviewed against a published rubric and returned with written feedback. The capstone goes through two review rounds before Demo Day.
- Project submissions per module with a deadline, not just attendance
- Rubric published to students and faculty: does it work, is it evaluated, is it deployed, can the student explain it
- Written feedback on every submission, with one resubmission allowed
- Originality checks on code and write-ups
- A verified project record per student, shared with the department and placement cell
Nothing taught in the classroom stays in the classroom.
Every on-campus session is recorded and published to the LMS, alongside the slides, labs, code, datasets, and quizzes. Students revise before exams and interviews; faculty see what was covered; the department sees progress.
- Recordings of every class, available within 24 hours
- Slides, lab notebooks, starter code, and datasets for every module
- Quizzes and project submission with grades and feedback in one place
- Progress dashboard for the department: attendance, submissions, scores
- Access continues for twelve months after the program, for students and faculty
The program hands your placement cell something it can use.
We work alongside your placement office. Each item below is a defined deliverable, so the cell knows what it receives and when.
Verified portfolios
Every student's deployed project, public repository, and reviewed project record, handed to the placement cell before drives begin.
Interview bootcamp
Mock technical rounds built on what AI-track recruiters currently ask, including the AI rounds large service companies have added, and how to present a project.
Recruiter Demo Day
Capstones presented to a panel of practitioners and, where the placement cell agrees, invited recruiters and your existing hiring partners.
Hiring network routing
Matching openings from CoreSmart's staffing and hiring-partner network are shared with the placement cell, and certified students are put forward.
Start with one stream. Extend to the rest on results.
The first engagement is one stream, one cohort, delivered by CoreSmart on campus, with success metrics agreed in writing before kickoff. If the metrics are met, the same mapping process extends to the other streams in the university.
One stream. One cohort. A mapped program, on-campus delivery, reviewed projects, and an outcomes report.
Runs as a credit-bearing elective where your governance allows it, or as a certified value-added course or summer intensive where it does not. Autonomous institutions that already run industry-connected elective baskets can carry CoreSmart the same way: a basket of three-credit electives with course outcomes, CO-PO mapping, and assessment scheme ready for the Board of Studies. Either way the delivery, the review, and the LMS access are the same.
What CoreSmart provides
- Syllabus and industry mapping workshop for the stream
- A CoreSmart trainer on campus, with teaching assistants for larger sections
- Hosted lab environment and model access credits per student
- Project review per module with written feedback, and capstone review
- LMS with all recordings and material, for students and faculty, twelve months
- Faculty orientation, Demo Day, placement deliverables, and outcomes report
- Documentation pack: MoU, syllabus, outcome mapping, assessment records
What the institution provides
- One starting cohort from a chosen stream, with a minimum enrolment agreed upfront
- Weekday timetable slots and lab or classroom access
- The current syllabus and a faculty contact for the mapping workshop
- A named sponsor from the dean, placement, or strategy office
- Placement cell participation in Demo Day and portfolio hand-off
Value-added route: four to six weeks from agreement to first class. Credit route: aligned to your Board of Studies calendar. Scope, metrics, and commercial terms are set in a short written brief before kickoff.
Per-student licence with a cohort minimum
Licensed per enrolled student, with a minimum cohort agreed per engagement. Pricing scales with volume and is quoted after the mapping workshop.
One-time mapping and customisation
Mapping to your syllabus and designing stream-specific or partner-domain capstones is scoped and priced once, separately from delivery.
Institution, student, or blended
Institution-funded, student-paid and collected by the college, or blended with a subsidy. We work with whichever model your finance office prefers.
Practitioners who build AI systems, teaching students to build them.
CoreSmart is an applied-AI education company with teams in Noida and Fremont, California. The campus programs are built from the same curriculum and projects as our 17-week Applied GenAI and Agentic AI Engineering program for working professionals, and taught by the same practitioner faculty.
Vinay Bamil, PhD
Designs the curriculum and leads the practitioner faculty. PhD in AI for Social Good, former GenAI coach at Google, and author of a forthcoming book on trustworthy AI systems.
Randeep S. Bhatia
Senior engineering leader and board director. Built and led teams at Audible, Twitch, and EA. Owns technology and platform direction.
Aashrith Arun Belawadi
Head of growth and strategy, MBA from the University of Bath, formerly at Deloitte. Leads the business and management programs.
Sanjay Lalwani
Data scientist at Siemens, formerly at Infosys, Azure certified. Brings enterprise data engineering and cloud depth.
Saurabh Sachdev
Data scientist and ML engineer with applied model-deployment experience across enterprise contexts.
Tanishq Gupta
Data scientist and ML engineer. Live-session instructor for CoreSmart's Applied GenAI and Agentic AI cohorts.
What deans, HoDs, and placement officers ask us.
Is this an online course?
No. Sessions are delivered in person on your campus by a CoreSmart trainer, on weekdays inside your timetable. The LMS holds the recordings, labs, and submissions so students can revise and work between sessions, but delivery happens in the classroom.
How exactly does the mapping to our syllabus work?
Before delivery, we run a mapping workshop with your department for each stream. We go through your current course structure, mark what is covered, partly covered, and missing against what recruiters are asking for, and agree the program content and capstones from that. The mapped syllabus is documented and shared with faculty before the first class.
Does this add work for our faculty?
No. CoreSmart delivers. Faculty receive a short certified orientation and full LMS access, so they understand what is taught and can support students, without having to teach the program. A faculty-delivered model is available later as a separate engagement.
Can non-CS and business students really do this?
Yes. Engineering streams build in code, with a short bridge module for students who need it. Business and non-CS streams run a no-code and low-code version with the same structure: a build per module, submissions, review, and a capstone. The gap map and the capstone are different for each stream.
How are projects reviewed?
Every module build is submitted on the LMS and reviewed against a published rubric with written feedback. Students get one resubmission. The capstone goes through two review rounds before Demo Day. The department receives a verified project record for each student.
What stays available after the program?
Everything. Recordings of every class, slides, labs, code, datasets, quizzes, and graded submissions remain on the LMS for twelve months for students and faculty.
Who pays?
Your choice: institution-funded, student-paid and collected by the college, or blended. Pricing is per enrolled student with an agreed minimum cohort, plus a one-time mapping fee, quoted after the mapping workshop.
Where does it sit in our curriculum?
Wherever your regulation already has room for industry input: professional elective slots, an industry-connected elective basket, skill enhancement or company-specific skills periods, prototype development or innovation labs, project work phases, and the internship credit. The mapping workshop identifies which slots apply to which year, so the program adds to the timetable instead of fighting it.
Can the capstone count as the final-year project?
In many autonomous regulations, yes. The capstone is scoped, reviewed, and documented to the standard of a project phase, with individual contribution reports for group work, and can be registered as Project Work Phase I or II or as a prototype development lab where the Board of Studies permits. We confirm this per institution during scoping.
How does it fit our calendar and accreditation?
Autonomous and private institutions can run it as a credit-bearing elective with a Board of Studies pack. Affiliated colleges run it as a value-added course or summer intensive. Both routes come with the documentation your IQAC, NAAC, and NBA processes need.
Do you guarantee placements?
No. We commit to defined placement deliverables: verified portfolios to your placement cell, an interview bootcamp, a recruiter-facing Demo Day, and routing of matching openings through our hiring network, with an outcomes report you can verify.
Request a syllabus mapping for your campus.
Tell us your institution, the stream you would start with, and your timeline. We will come back with a draft gap map for that stream, a proposed program outline, and success metrics. No commitment until the scope is clear.