The playbook for putting AI where the company works
Processes, company brain, agents, and governance: Yempik’s method for turning AI from a throwaway chat into an operating workspace. No hype: process first, then the tool; and the context stays yours.
Company brain
First understand where the company’s operating memory lives: decisions, rules, project state, exceptions.
Cowork
Then make that context readable: files, routines, and work state in a place the AI can reread.
Standard-First
When a flow matters, standardize it and build the agent only when data and governance can hold.
The blog is now a graph, too.
Explore problems, methods, proof, and tools by following relationships, not publication dates.
Every AI session produces decisions, rules, corrections. When it ends, they are gone. The problem, the evidence, and the requirements of an answer.
Experts ask for features and automation of the visible. The gold is in the solidified processes they never mention. The question that surfaces it at the first table.
A wiki is an archive that ages; a company brain is live context that people and agents use. When a wiki is enough, and how you move.
It’s not the model’s fault. What’s missing is the layer underneath: the company brain that gives context. Agents fail because they don’t know the company.
The person who’s good at a process can’t explain it. The method to pull it out: start from the real last time, dig into the "usually", write rules with a source.
We run Yempik on about 137 markdown files: decisions, state, rules, open questions. On the L0-L4 scale we’re at L4. Open source, zero lock-in.
Your company’s brain shouldn’t live rented inside a US vendor. For sensitive data and regulated sectors, owning it matters: files, GDPR, EU data.
Five levels, from “lives in heads” to “agent-operated”. Take the self-check and find your next step.
Access, data, human-in-the-loop, audit trail, evals, owner. The checklist we use to take an agent from demo to production, with numbers and sources.
The hard part is not creating folders: it is transferring knowledge between humans and machines. The method with 1:1 interviews, STT, files, and validation.
Decisions, rules, and work state are becoming AI memory. The question is not whether you need it: it is where it lives and who governs it.
Why AI stays a throwaway chat, and the method to give it a readable operating context with Cowork: structure, rules, routines. With a free kit and a mockup of the result.
What manual data entry costs, how to drop it starting Monday with Claude Cowork, and when a plain spreadsheet still does. With a calculator and a mockup of the result.
The formula, total cost of ownership, a baseline and control group to isolate the AI’s contribution, and a calculator to estimate payback and return.
The 4-step funnel to find the first process to automate: map it, filter with signals, find the constraint, score it with the scorecard.
The five steps to make a process repeatable and measurable before automating it: map the as-is, capture tacit knowledge, make the rules explicit, unify the data, write the SOP.
Adoption is the last link: without it, all the upstream work produces nothing. Champions, training in the flow, leaders who model the behavior, and how to measure real adoption.
Shadow AI, how to classify the risk, and the 30-day plan to govern AI in your company without a compliance department.
The real cases where we advise against AI: low volumes, unstable processes, AI for show, a broken process upstream. Honesty before the project.
What they actually do, lock-in vs your own code, costs, and when each one makes sense. A comparison with no marketing.
Want a company brain that stays yours?
We start from your process, make the operating context readable, and decide whether you need Cowork, a production agent, or just a better written rule.