Two people. The same two,
every cohort.
No rotating bench of freelance trainers, no guest influencer slot. The two people who teach the session are the two people doing this work the rest of the week — which is the only reason the examples in the room are real ones.
Purvansh Parmar
Builds the things. Makes them get found.
Purvansh is the founder of Food Never Comes, a product he built, shipped and grew himself. The important part for a classroom is not the brand, it is what building it required: writing the software, running the infrastructure, and then doing the unglamorous SEO work that decides whether anybody ever finds it.
That is the gap he teaches into. Most AI courses stop at "look what the model produced." Almost nobody covers what happens next — how an AI-generated page actually ranks, why Google quietly buries most of them, what technical SEO survives the shift to AI search, and how to wire a model into a product that real users hit every day without the bill or the latency getting away from you.
In the room he is the one who will open a terminal. He teaches agents, retrieval, automation and evals the way they behave in production rather than in a demo, and he is blunt about the cases where the honest answer is that AI is the wrong tool and a spreadsheet would do.
- AI engineering: agents, RAG, tool use, evals
- Automation in n8n and Make, in production
- SEO for AI-era search, and technical SEO
- Shipping and running a real product
- Cost, latency and the bill nobody budgets for
Burhan
Turns AI from a demo into a line item a business will sign.
Burhan works B2B. His day is spent in the places where AI projects actually succeed or quietly die — the enquiry desk, the back office, the sales floor, the accounts room — and his job is to tell the difference in advance.
He leads the discovery that begins every business engagement: the questionnaire and the call that work out what a team really repeats, scoring each process against volume, tedium and how rule-bound it is, then ranking the list by payback period. The output is deliberately unflattering. Most of them end with more processes recommended against automation than for it, because the fastest way to kill an AI programme inside a company is to automate the wrong thing first and spend the next year living it down.
In the classroom he teaches the commercial half that technical courses skip: how to size the opportunity, how to price AI work without giving away the margin, how to write a proposal a sceptical owner will sign, and how to get a team of twenty to change a habit — which is a harder problem than any model.
- Discovery and process scoring for a business
- Sector playbooks: hospitality, education, export, healthcare
- Pricing, proposals and the AI business case
- Change management for teams that have seen a failed rollout
- Sales, CRM and enquiry automation that sales teams actually use
Build and sell are different skills.
Most AI courses only have one.
A course taught only by an engineer produces people who can build something nobody asked for. A course taught only by a business person produces people who can sell something that does not work. Splitting the session between the two is not a staffing accident — it is the design.
Foundations, assistants, automation, production, and the SEO that decides whether anyone finds it.
The audit, what not to automate, pricing, the proposal, and getting a team to actually change.
Learn from both, this month.
Ninety minutes, live. You bring one job you repeat; you leave with a ranked shortlist.