Cloud & AI/ML Consultant · Corporate Trainer · Indore, India
AI/ML & RAG consultant — production data layer
Retrieval-augmented systems and LLM integration against documents, warehouses and databases you already run — with evaluation, and an honest stop when AI is the wrong tool.
Most enterprise AI work fails at the data layer, not at the model. Seventeen years on Oracle, Linux and AWS for Deutsche Bank, IBM, Wipro, Accenture, Capgemini, Cognizant and Oracle University is the background I bring into RAG and LLM projects — chunking, retrieval quality, permissions, and how you measure whether answers are actually useful.
What this consulting covers
RAG systems over enterprise documents — ingestion, chunking, embeddings, vector store choice, citations
LLM integration into existing apps and workflows (not demos that never leave a notebook)
Machine learning on operational and warehouse data when classical ML is the better fit
Evaluation design — how you will know the system works before you scale spend
Guardrails, least privilege and human-in-the-loop where actions have irreversible cost
An honest opinion on whether AI is the right answer at all
Who calls for this
Companies in India and remote teams that already hold the PDFs, tickets or warehouse tables
Engineering leads who need an LLM integration consultant without a six-month vendor theatre
Teams that want RAG built next to AWS or database work already underway — see also AWS consulting
Train the team on the same stack
RAG & LLM Applications training — build a working retrieval-augmented application over real documents. 30 hours · 5 weeks · live virtual · needs Python basics.
Open material on the site also covers AI fundamentals and agents — start from pushpjeet.com.
In the first message: what corpus or database, who will use the answers, and what “good” looks like. WhatsApp is fastest — same-day replies are typical.