Retrieval breaks silently
Pipelines that work in a demo degrade under real traffic patterns. Nobody notices until a customer does.
Turn your backend engineers into GenAI engineers.
PrasangLabs runs a cohort-based training program built inside your own tech stack — not a generic bootcamp. RAG, multi-agent systems, memory, and context and harness engineering, taught by shipping real production systems. Five to fifteen engineers, one real deployment.
Founding cohorts — enrolling now
Senior Machine Learning Engineer
Ex-Big Tech — enterprise-scale ML systems
- Production RAG, agents, and memory — not notebook demos
- Built for your stack, or fully custom on request
- Real curriculum, one real deployment
Built by someone who shipped this at scale.
PrasangLabs was founded after a career spanning McKinsey, Coinbase, and Walmart — from advising on strategy to shipping real AI agents at scale, to millions of users. The pattern was the same everywhere: an executive mandate to ship AI features, and an engineering org without the production infrastructure depth to do it safely.
Every cohort is taught by someone who has built and operated ML and GenAI systems at enterprise scale — not a curriculum licensed from a bootcamp and read off slides.
- Background
- McKinsey · Coinbase · Walmart — strategy to production ML
- Focus
- AI agents shipped at scale to millions of users, plus production RAG and MLOps
- Why this exists
- Every engineering org has an AI mandate. Few have the infrastructure depth to ship it without breaking something.
The hidden costs of scaling LLMs in production.
The failure modes look different from a slide deck than they do at 2 a.m. on call.
Bloated infrastructure spend
Token usage and vector-store sprawl grow faster than the value they produce, with no one owning the budget.
Tutorials that don't transfer
Bootcamp curricula assume a stack nobody in enterprise actually runs, and stop at "build a RAG demo." Real GenAI work also means agents that call each other, memory that persists across sessions, and context that doesn't rot.
Transitioning Backend Teams to GenAI Engineering.
Modules below are the default sequence — scoped to your stack, and reordered or swapped on request. A cohort of 5–15 engineers, working on your actual systems.
Enterprise MLOps & Eval Harnesses
Versioning, evaluation harnesses, and observability for LLM systems — built against your CI/CD, not a notebook. Your engineers leave able to answer "how do we know this is still working?"
Your stackRAG Architecture
Chunking, hybrid retrieval, and re-ranking that hold up outside a demo notebook.
Multi-Agent Systems
Orchestration, planning/execution loops, and agent-to-agent handoffs — with failure isolation so one bad agent doesn't take down the workflow.
Memory Systems
Short-term context state, long-term vector and structured memory, session persistence — and where memory ends and retrieval begins.
Context Engineering
Prompt construction, context-window budgeting, and structured grounding — the discipline underneath every agent that actually works in production.
Harness Engineering
Agent harnesses, sandboxed tool execution, guardrails, and human-in-the-loop checkpoints — for autonomous systems that run unattended in production.
Deployment & Observability
Canary rollouts, latency budgets, and cost guardrails for live LLM and agent traffic.
Capstone deployment
Your team ships one real feature into your own production environment, reviewed by your own stakeholders — not a graded assignment.
- 5–15 engineers
- 4–6 weeks
- Cohort-based
- Runs on your infra
- Vector stores & memory stores
- Orchestration & agent frameworks
- Evaluation & harness tooling
- Your CI/CD
Six engineers learn faster than one
A cohort debugs together, on the same codebase, in the same week. The knowledge stays on your team after we leave — it isn't locked in one engineer's head.
Not on the list? We scope it.
Tell us your stack and where your team is actually stuck. We build a custom curriculum around that gap instead of running the default sequence.
Custom scopingOne shipped deployment, across whichever modules your team needed. Not a certificate.
Prasang: context is everything.
Prasang is Sanskrit for context — what's relevant, here, now. Generic training data is noise without it.
We don't teach RAG, agents, or memory in the abstract. We open your repositories, your data contracts, and your deploy pipeline, and teach your engineers to build GenAI systems inside them.
Generic training
Build a RAG pipeline using LangChain and a vector database.
PrasangLabs
Build a RAG pipeline against your own Postgres + pgvector schema, under the same data-retention policy your compliance team already approved.
The next chapter: PrasangLabs as a platform.
Training is the first chapter. Next comes the infrastructure and tooling that takes a team from GenAI mandate to GenAI production, without a consultant in the room.
Coming soon
Request the Executive Briefing.
45 minutes. We look at your stack, your team, and whether this cohort is the right fit — no deck, no pitch.
- Walk through the full module list — or a custom one built for you
- See how we scope the program to your stack
- Get a cohort timeline and pricing