We help you take the lead — collaboratively and data-driven.
01
Create 50-200 test cases from your domain—real scenarios, edge cases, and variations. Not generic benchmarks, your actual data.
02
Compare 15+ leading AI models like OpenAI, Claude, Gemini, Deepseek and more. Track accuracy, cost, and latency for each.
03
Review detailed metrics, identify strengths and failure modes, and export presentation-ready reports for stakeholders.
04
See exact costs and projected performance before writing any code. Deploy the best setup knowing it works at scale.
PMs should lead AI feature development, it should not be defaulted to engineering.
Know which model, which prompt, which approach actually works for your product. Don't just default to the most popular LLM version today.
Test against real scenarios, not your best guesses. Catch failures before customers do.
AI development doesn't end at deployment. Monitor, collect edge cases, and refine—just like you iterate on UI.
The problem is people think what we get from AI is already a good answer. Because it does make sense somehow, right? I just read a study—I think 40% of the results from AI that we use is just nonsense. People don't think about that.
Product Leader, HR tech startup
For me as a product manager, when I finally got the value out of the product where I thought, 'I can try all my crazy ideas for AI features on my own—I don't need to depend on engineering or someone setting up an environment.'
senior pm, AI startup
The next question is infrastructure: 'Okay, Gemini is better for this. How do I implement this without making my infrastructure 15 times more complex because I have to plug in yet another model?'
CPO, fintech
Lovelaice transforms how teams evaluate and evolve AI features. All in one platform. For the whole team.
