“It was mind-blowing to see how the cost differences can be 60–100x between models. Having this data before shipping is crucial for us.”
Alicia Dick Wahlberg
Founder, Folksnest
The AI feature validation platform
Build AI features
worth the hype
Lovelaice gives product teams the tools to validate AI features before deployment, with real data, real test cases, and no engineering ticket required.
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.
01Catch
Catch it
before it ships.
Pipeline · Customer-queries · v3
Running
Failure clusters found
412 / 412 evalsInvoice fields · wrong schema18 hits
18Multilingual · answered in English6 hits
6Refund tone · too clinical3 hits
3Greeting drift · off-brand2 hits
229 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
Pipeline · Customer-queries · v3
Running
Failure clusters found
412 / 412 evalsInvoice fields · wrong schema18 hits
18Multilingual · answered in English6 hits
6Refund tone · too clinical3 hits
3Greeting drift · off-brand2 hits
229 failures · ClusteredReport ready · 2m 14s
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
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
03Own
Own the
quality story.
Quality · Release-trail · Q2
Fresh
Quality over time
92.4% nowFactuality · 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
Quality · Release-trail · Q2
Fresh
Quality over time
92.4% nowFactuality · 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
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.
Teams using LovelaiceReal teams · Real results
Built by product managers. Used by them, too.
“We had a gut feeling our results were good but no way to prove it. When we changed the prompt, we couldn't tell if it actually improved anything until Lovelaice.”
Viktoria Mall
Founder, Mind the brain
“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
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
comparisonStop guessing.
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.
What teams validate with Lovelaice.
Your use case is specific. Your test data is specific. Your quality standards are specific. Lovelaice works with all of it.
Chatbots & assistants.
AI answers that look perfect can be hallucinations. Test on real queries before users find them.
Learn more →Text generation.
Find the balance of quality, consistency, and cost across content types.
Learn more →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.
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.