Built by operators, for operators
A3 problem solving inside the AI assistant you already use. Describe the problem, and Lean AI works through the root causes and the fix with you.
The Challenge
Most fixes chase a symptom. The loudest complaint gets the budget, a fix goes in, and a month later the same problem is back, because no one stopped to ask why it was happening.
Lean AI puts a structured problem-solving method inside your assistant. It runs on A3 thinking: define the problem and the gap, find the root causes with evidence, then test a fix before you roll it out.
See it in action
Lean AI diagnosing a problem and working through root causes and solutions with a user.
WayaFrames
There are no commands to learn. Tell your assistant what's wrong in your own words, and Lean AI opens A3 thinking. Each framework facilitates a proven method with you, step by step, and you finish with a real deliverable.
A3 is the problem-solving method the other WayaFrames run inside. It pins down the problem, measures the gap between where you are and where you need to be, finds the root causes, and designs countermeasures to test. It adjusts its depth to the problem, from a fifteen-minute pass to a multi-week effort, and opens gap analysis, experiment design, or impact vs effort at the step where you need them.
Inside A3 thinking. Each one can also be used on its own:
Turns a vague, contested problem into a set of validated root causes. It tightens the problem statement against SMART-P criteria, then picks the method: 5 Whys for a contained issue, Fishbone when it crosses departments.
Turns root causes into small, testable experiments with clear success criteria, and runs the test, learn, and revise loop.
Ranks competing options (gaps, experiments, fixes) by impact against effort, so you act on the two or three that matter and park the rest.
Case Studies
The same methods Lean AI runs, used by our team inside eight companies.
Restaurant AP automation
We built invoice extraction into the app and moved from batches to one invoice at a time.
Read more →Food distribution · Warehouse
We tracked each picker's accuracy and checked only 1 in 10 carts for the most reliable pickers.
Read more →Food distribution · M&A
We found the warehouse system was never updated for the new racking, and rebuilt it while triaging by hand.
Read more →Food distribution · Finance
We linked each customer payment to its supplier invoice, so the supplier was paid straight from it.
Read more →14-person startup · Planning
We cut seven unranked goals to three objectives set on the real bottleneck.
Read more →Food distribution · Supply chain
We built the company's own warehouse and direct sourcing in 14 months, while the old business kept running.
Read more →Food distribution · Support
We built in-app ticket intake that routes each request to the right person automatically.
Read more →Hospital · GI suite
We sped up room changeover and fixed pre-op readiness so the next patient was ready on time.
Read more →How it works
A3 thinking runs the sequence and opens each step when you need it. Here's how it plays out on one problem.
Monthly revenue is stuck at $500K against an $800K target. Ops already cut its error rate from 50% to 5%, and revenue still hasn't moved.
Pin the problem down with data: what's happening, what should be, and how big the gap is.
Ops improved and revenue didn't, so ops isn't it. 60% of pipeline is unqualified, and those leads close at 3%.
Get from what to why, with 5 Whys or Fishbone, until the root cause is backed by evidence.
A 5 Whys chain traces the bad leads to a targeting spec no one owns.
Turn root causes into tests, rank them, run the best one, and watch the number.
Test a new targeting spec with a clear owner and track revenue against the target.
Revenue didn't move? Go back to Define with what you learned.
The figures are illustrative.
Lean AI defines the problem with you, finds the root cause, and helps you test the fix, inside the assistant you already use.
Sign Up for Early AccessFrequently Asked Questions
Lean AI is an MCP server, a small service you connect to your AI assistant. It adds a set of WayaFrames: guided problem-solving frameworks the assistant runs with you. You describe a problem, and it helps you define it, find the root causes, and test a fix.
A WayaFrame is a framework that runs inside your AI assistant. The assistant walks you through a proven method, asking the right questions in the right order, and you finish with a real deliverable: a clear problem statement, a set of root causes, or a ranked list of experiments.
Lean AI doesn't replace your AI assistant. It runs inside it. On its own, a general assistant answers whatever you ask, and it will suggest fixes before anyone knows the cause. Lean AI gives it a method: pin down the problem with numbers, find the root cause with evidence, then test a fix. Na'ama and Vince built that method scaling Cheetah to $150M in revenue and 350+ employees, and each session ends with a document you keep. It's the same assistant, following a method from people who have run a company.
No. Describe the problem in your own words, for example "help me figure out why our churn went up." Lean AI starts with A3 thinking and brings in gap analysis, experiment design, or impact vs effort when the problem calls for them. If you want one method on its own, ask for it by name.
Founders, operators, and leaders past the earliest stage: anyone with a problem that keeps coming back, improvement work that isn't moving the main number, or a decision that needs a diagnosis that holds up later. It works in any area: operations, sales, hiring, support, finance, product.
Lean AI is in early access. Sign up below and we'll contact you about getting connected. If you want a specialist to work through a problem with you, you can schedule a call.
An AI assistant that supports MCP, such as Claude. Once Lean AI is connected, bring it a problem and the assistant opens the right WayaFrame.
Join early access to get Lean AI in your assistant. When you want a specialist in the room, we're here.
Early Access
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