Ship AI features faster, with less risk

Test concepts, gather real performance data, and build conviction before committing engineering resources.

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AI product development is slower than it should be.

92%

Projects fail in production

70%

PMs have AI on their roadmap

<10%

PMs contribute directly to AI

A structured way for PMs to design, test, and validate AI features—before committing engineering efforts.

AI product development, simplified.

We help you take the lead — collaboratively and data-driven.

Build your test library

Create 50-200 test cases from your domain—real scenarios, edge cases, and variations. Not generic benchmarks, your actual data.

Run experiments

Compare 15+ leading AI models like OpenAI, Claude, Gemini, Deepseek and more. Track accuracy, cost, and latency for each.

Analyze performance

Review detailed metrics, identify strengths and failure modes, and export presentation-ready reports for stakeholders.

Decide confidently

See exact costs and projected performance before writing any code. Deploy the best setup knowing it works at scale.

AI product development is slower than it should be. Prompts live in code, evaluations hide in spreadsheets, and product teams wait on engineering to test ideas.

How Great AI Products Are Really Built

Lead with expertise

PMs should lead AI feature development, it should not be defaulted to engineering.

Replace guesses with data

Know which model, which prompt, which approach actually works for your product. Don't just default to the most popular LLM version today.

Test comprehensively

Test against real scenarios, not your best guesses. Catch failures before customers do.

Optimize continuously

AI development doesn't end at deployment. Monitor, collect edge cases, and refine—just like you iterate on UI.

Product teams using Lovelaice

“We developed the first prototype. That's awesome, but what are what are the limitations? What problems do we have? What risks do we have to take? ”

— Product Leader, HR tech startup

“Sometimes it gets it right, and eight out of 10 times it gets it right. But, these two times is where where it gets painful. What I want is results because that's how an LLM works is not what our customers pay for.”

— Senior PM, AI startup

“As soon as you approach a production state, it's like you need a way to really identify what is going to happen and whatnot. I love how Lovelaice can supports us to do that.”

— CPO, Fintech startup