Real learnings that generate better test hypotheses
The best Growth teams focus on learning. We turn results and documents nobody reads into a knowledge base focused on business growth!
In a landscape where only ~20% of experiments succeed on average, managing what you learn is worth its weight in gold.
Range consistent with published results from Microsoft (Kohavi et al.) and Booking.com on experiment success rates.
Generating and running tests, you already know how to do. Ganesha shows you the real reasoning behind why to test, and makes every decision generate learning for your next experiment cycle.
What Ganesha does
Diagnosis of your product's current stage
Not a summary of your documents. It's a read on where your product actually stands, versioned and auditable.
Hypotheses with cited sources
Every hypothesis points to the document, metric, or experiment that generated it. No source, no entry.
Mandatory counter-evidence
No hypothesis passes without the argument against it. Ganesha helps you confirm why it's the best decision.
ICE prioritization
Impact, confidence, and ease, with evidence strength as a composite indicator you can open up and check.
Feasibility check
Before you test, Ganesha checks whether your metric volume can actually sustain the test.
Memory
Every decision gets recorded. Together with the results, it improves the next cycle.
How it works
Step 1
Diagnosis
You define the cycle's goals and the context behind your thesis. Ganesha runs a full diagnosis of your product and data.
Step 2
Hypotheses
Ganesha generates prioritized hypotheses, with sources and counter-evidence. You edit, approve, or discard them, with a reason logged.
Step 3
Experiment
Approved hypotheses become experiments with a success criterion, financial impact, and full documentation.
Step 4
Learning
No experiment closes without a result, a learning, and a decision made. Everything is logged and feeds back into the next experiment.
You run the test in the tool you already use and collect the learnings in Ganesha's experiment dashboard. Every completed experiment feeds back into the cycle.
Integrate with your testing and analytics tool
AI suggests. You decide.
Autonomy without review is a promise of error.
Two sides, one product
For the PM running the cycle
Centralize your work in one place and turn Ganesha's knowledge base into your argument for WHY and WHICH hypotheses to test, with authority and confidence.
For leadership
Visibility into your teams' actions and results in one place. Track your team's progress and walk into next semester's planning with history, not opinion. Learning stays with the company even when the person doesn't.
You can already ask a general-purpose chatbot for hypotheses. So what changes?
A general-purpose chatbot only creates a hypothesis you have to trust. Ganesha creates a hypothesis with the source, the counter-argument, the strength of evidence — not a note the model made up. And the context doesn't die at the end of the conversation: it becomes a knowledge base, without hallucination.
Try it now