Corporate training is delivered under client confidentiality, so brand names are withheld. Every engagement below is real and described accurately by sector, scale, location, audience, and curriculum — enough for you to judge fit, without naming the client. If you need a reference for a specific program, that can be arranged privately after an intro call.
Delivered Engagements
Anonymized corporate AI training programs — scope, audience, and outcomes
1 · Agentic AI Product-Engineering Intensive
Client: one of the world's largest electronics manufacturers. Location: on-site, Noida (Delhi NCR), India. Format: 4-day hands-on intensive — condensed from a 13-day / 39-hour curriculum into a focused delivery (80–90% hands-on).
Audience: Python, .NET, and Java developers, QA / test engineers, DevOps & SRE, data analysts, and solution architects — a mixed, enterprise engineering cohort.
Scope: product thinking and agent foundations; single-agent engineering (memory, context, domain customisation); RAG and knowledge engineering (document intelligence, CI/CD and root-cause-analysis agents, enterprise knowledge assistants); and multi-agent productization (orchestration, reliability, observability, governance, human-in-the-loop). Built on LangGraph with a Microsoft Autogen track and language-agnostic integration for .NET engineers.
Outcome: every participant designed and demonstrated their own production-oriented agentic AI prototype aligned to their domain — no group projects, no toy demos.
2 · AI for Manufacturing
Client: one of the world's largest electronics manufacturers. Location: on-site, Noida (Delhi NCR), India. Format: 4-day hands-on, concept-first with live coding on real production data.
Audience: production, quality, and process engineers, plus plant IT — largely new to AI, working daily with MES/ERP data, OEE, and inspection lines.
Scope: demystifying AI for the factory floor (ML, deep learning, GenAI, agentic AI; Industry 4.0/5.0); the data journey to AI-ready information; calculating OEE, MTBF and MTTR from raw data in Python; AI-assisted and GenAI reporting with live dashboards; pattern and anomaly detection; predictive and prescriptive analytics for maintenance; and computer vision (AOI, solder-paste inspection, OCR).
Outcome: engineers replaced manual Excel-and-email reporting with automated pipelines, and left able to build predictive and vision-assisted quality workflows on their own data.
3 · LLM / SLM Engineering for Software Engineers
Client: the software R&D arm of a global electronics conglomerate. Location: on-site, Bengaluru, India. Format: 4-day classroom program, concept-first with live code demonstrations and supervised hands-on — a single trainer with a 30-engineer cohort.
Audience: experienced front-end and back-end engineers (5–10 years) new to AI, some with prior exposure.
Scope: transformers and self-attention (Q/K/V, positional encoding, multi-head attention) from first principles; RAG with hallucination control, chunking strategy, and live guardrails against PII and unsafe actions; dialogue management for multi-turn, task-completing assistants; and LLM/SLM optimisation — quantisation trade-offs, KV-cache, and LangGraph slot design. Hands-on used live parameter mutation (chunk size, top-k, KV-cache toggles) so engineers could watch behaviour change in real time.
Outcome: a senior engineering cohort moved from AI-curious to able to reason about, build, and ground LLM/SLM features responsibly.
Read the full case study → · RAG Training → · LangGraph Training →
4 · Enterprise AI Capability Programmes (Builder to Principal & AI Navigation)
Client: withheld under confidentiality (identifying details removed from the source brief). Format: two level-based programmes, ~80% hands-on, with placement diagnostics, capstones, mentoring, and a monitored live period.
Audience: technical practitioners from entry (L1) through principal/expert (L5), plus context authors, governance leads, and senior leaders in separate non-coding cohorts.
Scope: an IT-team AI enablement track from Builder → Specialist → Architect → Principal (prompting and context engineering, RAG and agentic RAG, MCP and agent-to-agent infrastructure, evaluation-driven development, guardrails and governance, LLMOps/MLOps, and LoRA/QLoRA fine-tuning); and an AI Navigation programme spanning AI Tool Practitioner, Context & Instruction Design, Agent Engineering, AI Governance, and a leadership track.
Outcome: a leveled path that takes an organisation from first agents to principal-level standards, and from tool users to governance and evidence-based AI investment decisions.
Beyond These Engagements
A longer record of training teams and thousands of learners
Alongside the corporate programs above, training and mentoring has reached teams and cohorts at PhysicsWallah, TalentSprint, Simplilearn, Masai School, Nolan EduTech, NIT Jalandhar, and IIT Kanpur — thousands of engineers across agentic AI, RAG, backend systems, system design, and interview preparation. See teams & institutions trained for the full picture.
Explore the Programs
AI for Manufacturing →
Predictive analytics, OEE in Python, and computer-vision inspection for factory teams.
Agentic AI Training →
End-to-end agent stack with LangGraph, tools, memory, guardrails, and evals.
RAG Training →
Production retrieval: chunking, embeddings, pgvector, grounded answers, evals.
Corporate AI Training →
Team upskilling for engineering and L&D — build real in-house AI capability.