The AI product validation platform

Build AI products
worth the hype

Lovelaice is the product analytics platform for AI features. Validate AI features before deployment with real data, real test cases, and no engineering ticket required. Moving from idea to validated production-ready configuration in days instead of months.

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Lovelaice product analytics dashboard for AI features

Trusted by teams shipping AI in production

  • Operations1
  • AI Sentinel
  • LeanOnMe
  • Wittlinger
Silent failures

Your AI doesn't fail loudly.
It fails quietly, while users churn.

Most AI failures have no error log. No alert. Just a feature that quietly underperforms while users quietly leave.

The people who know what good looks like are locked out of testing and validating AI quality, because it defaults to engineering.

Ship & pray isn't a strategy.

If you've said one of these recently, you're not alone.

"It's on the roadmap."

You've been waiting on engineering for two quarters. Every AI idea needs a ticket, a sprint, a review. The ideas pile up. The shipping doesn't.

"We vibe-checked it. It's probably fine."

You tested three happy cases and shipped. You'd rather not find out what broke from a customer complaint.

"How's our AI doing?" "Good question."

Leadership wants a number. Your monitoring is Slack threads. The dashboard you need doesn't exist.

"We upgraded the model. Then the complaints started."

You found out a month later, from users. There was no alert, no comparison, no process. Just inbox messages and a very uncomfortable sprint review.

See where your team stands
01Catch

Catch it
before it ships.

Pipeline · Customer-queries · v3
Running

Failure clusters found

412 / 412 evals
Invoice fields · wrong schema18 hits
18
Multilingual · answered in English6 hits
6
Refund tone · too clinical3 hits
3
Greeting drift · off-brand2 hits
2
29 failures · ClusteredReport ready · 2m 14s

Most teams test a handful of cases that already work. Lovelaice runs evals across your full dataset, clusters the failures, and hands you the short list of what actually breaks.

100+test cases ran
24failure groups
2mavg run time
See how it works →
02Validate

Prove it
moves the number.

Experiment · Compare · Head-to-head
Live

Head-to-head on your data

Same prompt · Same 127 cases

Current · GPT-4.1
47%
$2.90/k · 2.1s
→
Candidate · Sonnet-4.5
89%
$2.10/k · 1.4s
Accuracy
+42pt
Cost per call
-27%
Latency
-33%
Validated · 412 runsRecommend → Sonnet-4.5

Every prompt tweak and model swap gets a before/after score on the same test set. No more shipping a change and hoping the complaints stop.

+42ptaccuracy gap found
27%cost saved
1click to deploy
See how it works →
03Own

Own the
quality story.

Quality · Release-trail · Q2
Fresh

Quality over time

92.4% now

Factuality · Last 8 releases

Accuracy
92.4%
↑ 14pt vs baseline
Spend
$13.8K
1.3k calls · $0.01
Latency
1.2s
p50 · steady
Release 04-12 · -4.2%
Auto-exports
PDFCSVNotion

Dashboards PMs can read without asking engineering. Regressions flagged the moment they land. The full trail — exportable, timestamped, defensible.

-4.2%regression caught
12min to export
0eng tickets
See how it works →

Teams using Lovelaice already know.

Accuracy lift

0%

In a single iteration. Under an hour.

Time to validation

0 days

From idea to configured. Vs. 8–14 weeks in the traditional PRD loop.

Return on investment

0×

In month one. At €499/month.

Cost reduction

>0×

A fintech team switched away from GPT-4.1. Same task, 10× cheaper, higher accuracy.

Not sure where to start?

Take our 3-minute AI evaluation quiz and get a personalized report on your team's AI maturity level and how it compares to our benchmarks.

Take the quiz
Teams using Lovelaice

Built for real impact. Used by great teams.

Real teams · Real results
“The Lovelaice team was able to go very deep into investigating the AI feature on their own. Our internal resources were only used for very concrete feedback, so it was very efficient for us. I was also very happy to see how the process made it easy for our product manager to contribute directly and feel empowered to shape the AI. The model we were already using turned out to be a good fit for our case, the new prompt and setup were what increased the quality.”
Anian Ziegler

Anian Ziegler

Co-Founder & CPTO, Operations1

“When we talk about AI in Fintech, particularly in critical areas like investments or purchasing, forecasting, financial reporting, the stakes can be the life or death of a company. Having systematic, reliable procedures for testing and measuring quality is critical. That’s why I find Lovelaice to be so valuable and important in the mission of AI Sentinel but also for the broader European AI products landscape. I value the collaboration with Lovelaice team to build AI Sentinel to ensure it meets the high quality bar for our customers.”
Daniel Kurt

Daniel Kurt

Founder, AI Sentinel

“It used to take us 3-4 days to a week or more to run a new iteration on the prompt and get the new results. With Lovelaice we cut this time to few hours, and product managers can do it without an engineering ticket.”
Albert Cristea

Albert Cristea

Director of products

· Product feature ·

See Lovelaice run in real workflows.

Discover how product teams experiment with AI models, compare results, and gain actionable insights — all within a collaborative experimentation environment.

  • ✓Test AI ideas using real data
  • ✓Compare multiple models instantly
  • ✓Gain automated performance insights
  • ✓Share validated knowledge across teams
03:12 · walkthrough
Lovelaice
comparison

Stop guessing.
Start knowing.

Ship & hope

  • ×Pick Opus 4.7 because "it's the best"
  • ×Test 3–10 happy path examples
  • ×Find out what broke from user complaints
  • ×PMs waiting on engineering for every change
  • ×Paying up to 100x for models without benchmarking
  • ×Learnings in scattered Slack threads

With Lovelaice

  • ✓Compare 10+ models on your actual data
  • ✓Evaluate across hundreds of real test cases
  • ✓Catch failures before deployment, scale manual testing
  • ✓Product managers run experiments independently
  • ✓Know your actual cost per use case before you commit
  • ✓Run hundreds of test cases without time limits, every time.
Three steps to shipping proven AI

Three steps. Not a two-quarter project.

Upload, evaluate, decide. Before any engineers are involved.

Upload your data.

Bring your real test cases, prompts, and quality criteria. No synthetic data, no happy-path assumptions.

Run structured evaluations.

Test across models, catch failures by category, compare results side by side. Your team does this. No engineering required.

Ship with confidence.

Hand engineering a proven configuration with accuracy, cost, and latency data attached. Or keep iterating until it's right.

Real AI products. Real numbers.

How product teams use Lovelaice to turn live AI products into measured, reliable systems.

— Eval setup & optimization

52% to 88% accuracy. Three weeks.

Measurable quality uplift on the model they were already running — starting from zero baseline.

Read the case study →
— Eval setup & optimization

18% to 93% accuracy. 25 experiments.

Production-ready agent on GPT-5 Mini at 93% accuracy — on a cheaper model than any earlier attempt.

Read the case study →
— Eval audit & optimization

The failure no one was looking for.

A systematic multi-run pattern analysis surfaced a consistent bias in a validated golden set.

Read the case study →
View all case studies →
Ready when you are

Your AI is live.
Do you know it's working?

Not hoping. Not guessing. Knowing. Bring your data, run your first evaluation, and see results in one session.

Start for free

Not ready to demo? Take the 3-min diagnostic instead.

FAQ,
briefly.

Still on the fence? Here's what most teams ask before their first eval run.