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Manwe
Decision intelligence for consequential choices
Manwe is decision intelligence for consequential choices. It builds an ontology around the question: actors, evidence, assumptions, risks, scenarios, and watchpoints, then pressure-tests the plan into a decision record. Advisors, trials, and model teams expose dissent instead of smoothing it away. The result is a verdict, pressure map, forecasts, and next actions across the web app and Mac beta.
Manwe is built on a simple conviction: the most expensive failures rarely come from a lack of answers. They come from invisible assumptions, collapsed context, false consensus, and decisions that were never forced to withstand pressure before the organization acted.
Most AI products optimize for response. Manwe optimizes for decision quality.
Every serious Manwe run begins by imposing structure on the problem. It builds an ontology of the decision: actors, constraints, incentives, claims, evidence, assumptions, scenarios, risks, watchpoints, and what would change the verdict. Then it pressure-tests that map through advisors, trials, dissent, and model teams.
The output is not just another answer. It is an accountable decision record: what was believed, what was contested, where the evidence was strong or weak, why the recommendation moved, what futures should be watched, and what action should happen next.
The web app is for fast decision records. The Mac beta goes deeper with Worlds, governed memory, What Manwe sees, scenario testing, local/cloud/custom models, and Diagnostic Trials, where a recommendation is put on trial instead of being allowed to sound confident in private.
The feedback I care about is direct:
Where does Manwe expose risk that ordinary AI chat hides?
Where does the decision record need to become sharper before operators would trust it for consequential work?
About Manwe on Product Hunt
“Decision intelligence for consequential choices”
Manwe was submitted on Product Hunt and earned 4 upvotes and 2 comments, placing #52 on the daily leaderboard. Manwe is decision intelligence for consequential choices. It builds an ontology around the question: actors, evidence, assumptions, risks, scenarios, and watchpoints, then pressure-tests the plan into a decision record. Advisors, trials, and model teams expose dissent instead of smoothing it away. The result is a verdict, pressure map, forecasts, and next actions across the web app and Mac beta.
On the analytics side, Manwe competes within Productivity and Artificial Intelligence — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Manwe performed against the three products that launched closest to it on the same day.
Who hunted Manwe?
Manwe was hunted by Oncel Cebeci. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Manwe including community comment highlights and product details, visit the product overview.
Oncel Cebeci
Hey Product Hunt, I am Oncel, maker of Manwe.
Manwe is built on a simple conviction: the most expensive failures rarely come from a lack of answers. They come from invisible assumptions, collapsed context, false consensus, and decisions that were never forced to withstand pressure before the organization acted.
Most AI products optimize for response. Manwe optimizes for decision quality.
Every serious Manwe run begins by imposing structure on the problem. It builds an ontology of the decision: actors, constraints, incentives, claims, evidence, assumptions, scenarios, risks, watchpoints, and what would change the verdict. Then it pressure-tests that map through advisors, trials, dissent, and model teams.
The output is not just another answer. It is an accountable decision record: what was believed, what was contested, where the evidence was strong or weak, why the recommendation moved, what futures should be watched, and what action should happen next.
The web app is for fast decision records. The Mac beta goes deeper with Worlds, governed memory, What Manwe sees, scenario testing, local/cloud/custom models, and Diagnostic Trials, where a recommendation is put on trial instead of being allowed to sound confident in private.
The feedback I care about is direct:
Where does Manwe expose risk that ordinary AI chat hides?
Where does the decision record need to become sharper before operators would trust it for consequential work?