Golden Gecko uses AI to help you at every stage - from choosing experiments to interpreting results. Here's how each AI feature works.
Playbook matching
When you create an objective, AI analyzes your description and recommends relevant playbooks. It considers:
- •Keywords and intent in your objective
- •The funnel stage you're targeting
- •Patterns from similar objectives
- •Playbook difficulty vs. typical resources at your stage
Signal-based suggestions
When you log signals on an objective page (observations about your product, customers, or competitors), AI uses them to generate more relevant experiment suggestions. Instead of generic ideas, you get experiments grounded in what you've actually observed.
- •Signals are included as context when generating any AI suggestions for that objective
- •The "Get experiment ideas from signals" button generates experiments specifically targeting your observations
- •Post-experiment "what to try next" suggestions also consider your signals
- •More signals = better suggestions. Log what you notice, even if it seems small
Business context in AI
All AI features use your project's business context (industry, stage, audience, company summary) to give more relevant suggestions. Set this once in project settings — it automatically feeds into playbook matching, experiment suggestions, sparring questions, result analysis, and the growth assistant chat.
AI sparring
When you log experiment results, AI acts as your sparring partner — asking 2-3 targeted follow-up questions that push you to dig deeper. Your answers enrich the analysis for more specific, actionable insights.
- •Questions are specific to your experiment type (outreach → messaging, A/B test → variants)
- •An optional second round asks even deeper questions based on your answers
- •You can skip sparring and go straight to saving if you prefer
- •Pro feature — free users can still save results without sparring
Result interpretation
After logging experiment results (and optionally answering sparring questions), AI interprets what happened and what to do next:
- •Analyzes your results against the expected outcomes
- •Uses your business context for industry-relevant interpretation
- •Suggests possible explanations for the results
- •Recommends follow-up experiments or iterations
Tip: AI interpretation is a starting point, not the final answer. Use it to prompt your own thinking, then refine based on your context.
Learning extraction
AI helps extract learnings from your experiments - patterns, insights, and institutional knowledge that compounds over time.
- •Identifies patterns across multiple experiments
- •Suggests hypotheses for future tests
- •Highlights what's working (and what's not) for your product
- •Builds your growth knowledge base automatically
Privacy and data
Your experiment data is used only to provide AI features within your account. We don't train on your data or share it with third parties.