Cohort 4 helps you package a practical AI-first company method into a sellable business offering: install the organization layer that connects sources like meetings, emails, CRM, notes, and documents into a knowledge base any LLM can use.
Most companies are not ready for AI because their knowledge is scattered across meetings, inboxes, Slack, LinkedIn, CRM notes, shared drives, and people's heads. Cohort 4 starts with a service businesses can understand and buy: an Enterprise Second Brain that connects sources, organizes the knowledge base, and makes institutional memory usable by humans and any LLM of choice.
CRM stores records. Shared drives store files. A second brain stores connected context. If a PM, salesperson, or founder leaves, the company should not lose the customer history, meeting context, follow-up logic, source links, and deal intuition that lived in that person's head. That pain is the wedge: every business that forgets what it knows is a potential customer.
Jane shows the pattern. She used AI, Notion, and workflow tools to turn scattered work into leverage. That works for one person. The next challenge is making it work for a team: shared memory, shared customer context, and AI agents that can retrieve and act on what the company already knows.
That is Cohort 4's starting point: learn the method, build the base system, then package it as a productized service you can sell to a specific industry.
The Enterprise Second Brain is not locked to one app. Sources like email, meeting transcripts, CRM, notes, and documents flow into a knowledge base that can live in Obsidian, Box, Dropbox, Google Drive, GitHub, or another storage layer. Hermes sits in the middle as the organization layer — keeping the KB structured, source-linked, current, and readable by Claude, Copilot, ChatGPT, or any LLM the customer chooses.
Email, meeting transcripts, CRM, notes, documents, Slack/chat, customer conversations, and other business systems where context starts.
Discovers, scans, extracts, enriches, organizes, and writes source-linked knowledge on a regular basis.
The KB can live in Obsidian, Box, Dropbox, Drive, GitHub, or shared folders — then Claude, Copilot, ChatGPT, or another LLM reads the organized memory.
Most AI programs teach prompts and tools. We start with the operating layer every AI-ready business needs: how a company captures knowledge so AI can use it. Then we push you to package that layer as an offering and bring back evidence from real business buyers.
A working base system that connects sources — meetings, transcripts, email, CRM, notes, docs, and shared storage — into one organized company memory.
Apply the second-brain method to a specific industry and define the business offer: who has the pain, what knowledge is lost, what you install, and why they would pay.
Real conversations with real potential buyers — not friends who say it sounds cool. Come back with quotes, signals, objections, and a clear answer to: "Would a business buy this?"
Why this sequence? AI-first is too abstract until you can sell the first layer: company memory.
Once meetings, notes, transcripts, emails, CRM records, and customer context are organized into a connected KB, Claude, Copilot, ChatGPT, or any other LLM can summarize, index, retrieve, remind, and act. That is a concrete business outcome — easier to explain, easier to demo, and easier to sell than another dashboard, CRM, or prompt library.
This is how we find the right people. Not the ones who follow instructions perfectly. The ones who move anyway.
The new founder stack is already here. The Andrej Karpathy LLM-wiki pattern points to a simpler future: source-linked knowledge, AI-created indexes, links back to sources, and agents that can read the company's memory. Obsidian can be the starting interface, but the KB can live in Box, Dropbox, Google Drive, GitHub, or whatever storage layer the customer already trusts.
Group sessions give you the shared playbook, peer momentum, and accountability that comes from building alongside other founders. 1-on-1 office hours sharpen the work to your specific business — the same way Sze works with founders inside our portfolio companies. Both, not either.
Most programs end with Demo Day — a polished pitch to investors. We end with Customer Day — a focused, evidence-first session where each founder presents what real customers actually said. Quotes. Objections. Signals. Money on the table or money refused. The bar isn't pitch quality; it's truth quality.
Anyone can be impressed by an AI demo. The harder thing is proving that a real company has a painful knowledge problem, that your second-brain approach fits their workflow, and that someone would buy the installation or service.
Traditional accelerators write big checks because you need to hire a team. We pay $10K because you don't. AI agents compressed what used to cost half a million to build. That's the entire premise of Zenith — and the reason our number looks small. It's small on purpose.
Accelerators give you a class and a small check. Studios co-found the company with you — daily work, infrastructure, network, the whole apparatus of starting up. We're the second one. The equity reflects three to four months of founder-level work, not the size of the check.
This is the work the equity is for. Not a class. Not coaching. Co-founder work, done daily, for three to four months.
Three to four months of working together on product, customer development, and GTM. Not weekly check-ins. Daily rhythm.
Entity setup, banking, legal foundation, equity structure. The unglamorous infrastructure that takes founders months to figure out alone.
Accounting, payroll, contracts, vendor setup. We hand you a running company, not a to-do list.
The AI agent stack, deployment pipeline, and engineering foundation that lets one person ship like a team.
Warm intros, pilot conversations, advisor introductions. The network that took us 17+ years to build, applied to your company.
When you're ready to raise external capital, we set you up — investor intros, pitch prep, terms guidance. We've done it.
Zenith works in four phases. The incubation program is Phase 1 — the filter. Founders who get through unlock everything that follows: a real company, real capital, and a path to scale.
4 weeks. Build the base offer + prove buyer demand. Ends with Customer Day.
Zenith reviews your Phase 1 output. Go / no-go decision on co-building.
Zenith joins as your co-founder. We invest, we set up, we work daily. Goal: turn the offer into first paying customers.
After first dollar, raise $100K externally. Path to $1M ARR.
People who came in without a traditional technical background and used AI to turn their own work, domain insight, or community knowledge into something real.
Non-technical PM. Replaced what would have been a CTO and full dev team using AI agents. Now leading product and closing deals in B2B lending.
Seasoned trader with no coding background. Built an investment research platform from scratch — never written a line of code or used AI before joining.
Built a guitar pedal design tool for the DIY community he's part of. Built for himself first, then for people exactly like him.
Applications close July 11. Cohort 4 starts July 13. Customer Day August 9. Free to apply.
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