Product management, minus some of the politics
Give your agent the goals your company agreed on and the context behind them.
Ask what to bet on. See the evidence. Make the call.
Now your decisions have receipts.
Works with Claude and ChatGPT. Bring whichever agent you already argue with.
Example conversation in your agentIllustration — not a live session, and not anyone's real numbers
Try it before you connect anything
Copy this into ChatGPT, Claude, or the agent you already use. It reads the public Golden Beans guide, explains what it can do, then offers to run the North Star workshop with you.
No account. No MCP. No tiny onboarding hostage situation.
When you want the work to persist — or your agent to use live product context — that's when you connect Golden Beans.
Read https://golden-beans-gamma.vercel.app/llms.txt and act as my product-thinking partner for this conversation. First, explain Golden Beans in plain English: what it is, what it gives my agent, and what it deliberately does not do. Keep it brief and don't sell me. Then give me two choices: 1) ask questions about Golden Beans, or 2) run the North Star workshop now. If I choose the workshop, read https://golden-beans-gamma.vercel.app/northstar-self-serve.md and facilitate it one question at a time. Use my answers, challenge vague language, and keep the goal measurable. At the end, summarize the proposed North Star, its inputs, guardrails, assumptions, and the first things we should test. Do not claim you are connected to my Golden Beans workspace unless I have actually connected the MCP. The Golden Beans MCP connector is read-only: even once connected you can read my product context, but you cannot write my North Star or save this conversation. If we finish useful work, say so plainly — tell me the summary lives only in this chat, and that I can set the North Star up myself in Golden Beans so that you have it as ongoing context next time.
How it grows
No migration project. No new place for everyone to stare at charts.
Golden Beans gives your agent the product context it needs to work alongside you.
Start with the goal your company can actually agree on. Your agent now has something better than an opinion to work from.
One number. Fewer philosophical debates.
Connect the places that know what customers did, what shipped, and what it cost. Your agent can finally see the same product you do.
No new dashboard required.
Ask what to bet on, what changed, whether it worked, or what you should look at next. The answer comes back against the goal.
With receipts.
We considered adding a twelve-week implementation phase here. It tested poorly.
Sales heard it from a prospect. Engineering knows where the bodies are buried. The CEO has seen this movie before.
None of them are necessarily wrong.
A good idea shouldn't become a great idea because someone outranks the room.
Seniority is useful context. Not a confidence interval.
Retention says yes. Revenue says maybe. That dashboard nobody fully trusts says absolutely.
Usually you. Preferably before the meeting about the meeting.
Opinions aren't the problem.
Good product teams should disagree. Golden Beans gives your agent the same quantifiable goal to test those ideas against — regardless of who suggested them.
The idea gets a fair hearing. The org chart doesn't get a vote.
Your agent can compare the bets against the goal, check what happened before, and work out what waiting could cost.
It doesn't care whose idea it was. Neither does the math.
Example conversation in your agentNot a Golden Beans chat screen
Much nicer than “because Steve really believes in it.” Steve is probably lovely.
Golden Beans connects what customers do, what your team ships, what you bet on, and whether it worked.
It already has the receipts.
See expected impact, confidence, effort and the cost of waiting.
Bet with a case
You choose who gets it, how much, and when.
Your call
See whether the thing you shipped moved the number it was supposed to. Not whether the launch announcement collected twelve celebratory reactions.
See the decision, rollout and result without branches, rebases, or Git archaeology.
In Golden Beans /app · releasesIllustration — the product UI, not anyone's real releases
Release legibility
See what changed in human language: who saw it, what happened, and whether anything still needs your decision.
Your agent can investigate, compare, challenge and propose. Anything that changes the product gets staged first.
Then you make the call.
In Golden Beans /app · releasesIllustration — your agent proposes, you confirm, with your own data
Your agent suggested a small first release. 1 in 10 eligible customers in Mexico would see the new checkout; everyone else stays on the current version while you watch the result.
Why this size?
Enough traffic to learn without putting the whole market behind an unproven change.
What are we watching?
Expected North Star lift: +4–7% · current confidence: 82%.
Because “the AI did it” isn't much of an audit trail.
Every write stages first. Every credential is scoped. Every action leaves a trail.
Autonomy is great. Surprise production changes less so.
Live today: the staged write tools are switched on, and every one of them still requires your explicit confirmation before it applies. SHIPPED · signals-loop
You don't need Golden Beans to replace your engineers, analysts, executives — or your judgment.
You need less work between having a question and being able to make a good call. That's leverage.
Without Golden Beans
Question
Find the dashboard
Ask analytics
Ask engineering
Meeting
Meeting about previous meeting
Decision
We considered adding another meeting here for realism.
With Golden Beans
Question
Ask your agent
Inspect the case
Decision
Your calendar may experience side effects.
Otherwise this is just a nice landing page.
Pod report
Computed, not claimed — every figure below comes from golden-beans's own git and pull-request history, measured over 25 days and 199 commits. Nothing here is estimated, and the things we cannot measure are listed beside the things we can.
Not instrumented here: velocity (points per sprint) · cost per shipped point · change failure rate · failed-deployment recovery time · throughput (stories per period). Each gap names the guardrail that would close it — which is most of what a pods engagement installs.
COMPUTED · pod-report
The engine, live
A real read, performed just nowNot an illustration — the same queries your agent would run
Everything below is rendered from the synthetic golden-beans-demo project by the same queries your agent would run.* No client data appears on this page, ever.
* These numbers are independently checkable: /api/v1/public/north-star is public for the demo project and returns the same underlying data. Curl it mid-meeting.
Your warehouse probably works just fine too. Connect them. Normalize the concepts. Give your agent safe access. Build approvals. Correlate releases to outcomes. Maintain the glue.
Or don't.
| Concern | Stitch it yourself | Golden Beans |
|---|---|---|
| Your agent knows | Whatever you've wired up | The product context behind the decision |
| A bet becomes | Work across several systems | One case against the goal |
| An action becomes | Whatever each tool permits | Staged → confirmed → logged |
| What shipped | Somewhere in the repo | One line a PM can actually read |
| Did it work? | Back to the dashboards | Back to the original bet |
Golden Beans isn't another place to analyze your product. It's the product layer your agent was missing.
Claude person? Great. ChatGPT person? Also great.
Golden Beans isn't here to start another AI preference war. We have roadmaps to ship.
Add your Golden Beans MCP connection and start asking product questions from Claude.
Connect to ClaudeConnect the remote MCP and let ChatGPT work from the same product context.
Connect to ChatGPTAvailability and capabilities vary by plan. Yes, we also wish these sentences aged more slowly.
Point a compatible agent at Golden Beans. Your product context shouldn't depend on which model won Twitter this week.
Read the MCP docsA few lines get the first event in. Your engineers keep control of what enters Golden Beans. You get a product layer you and your agent can actually operate.
Everybody gets to keep their IDE.
npm install @golden-beans/sdk
Start with one project for $0. Bring the rest of the company when they inevitably ask where you got the numbers.
Humans remain unmetered.
A handful
$0
One project. Enough Golden Beans to find out whether we're onto something.
No credit card. We checked.
MOST PLANTED
The beanstalk
$49/mo
For products that kept growing.
There is no billing rail yet — nobody can be charged this today. It is what the tier will cost, published early so you can plan against it. Start on the free tier; we will not move you onto a paid plan without asking.
The vault
Pods
Your team, augmented with the same ways of working we use ourselves. Benchmarked before and after, because “it felt faster” isn't a Pod Report.
Talk to usWe meter events. Not coworkers. Per-tenant limits are set on the tenant, not by this page — raising one is a database change, never a redeploy. (multi-tenant-activation)
Still deciding?
Ask your agent whether Golden Beans is likely to move your North Star.
At least one of us should practice what we preach.
Read https://golden-beans-gamma.vercel.app/llms.txt. Based on what Golden Beans actually does, help me decide whether it could improve how I manage product decisions and move our North Star. Ask me only the minimum questions you need about my product, current decision process, and where context gets lost. Then give me: (1) where Golden Beans would create leverage, (2) where it probably would not help, and (3) whether the next sensible step is the North Star workshop or connecting Golden Beans. Don't pretend you have access to my workspace unless I connect it.
Free to try. No credit card. No sales call mysteriously disguised as a “quick chat”.