AI quality evaluation platformBuild AI products
you can trust

Findings generated by another team get argued with and shelved. The team accountable for the outcome has to own the measurement, or the measurement doesn’t change decisions. Lovelaice is built so the people who know what a correct answer looks like can run the evals themselves and bring engineering a validated spec instead of a vague ticket.

Not from a framework. Teams start by reading a few dozen real outputs, noting what’s wrong, and grouping the notes into quality dimensions. The rubric comes out of the data. Predefined categories hide the failures you didn’t expect.

An A/B test tells you whether an outcome moved. On an AI feature it takes weeks, costs more, and the result stops holding when the model changes. An eval tells you whether the feature produces correct output, before it reaches users and again after every change. Use both: evals for quality, A/B tests for value.

No. Lovelaice is built for teams that are building AI competency, not teams that already have it. If you can describe a problem and upload examples of good and bad outputs, you can start running experiments. The platform walks you through the process, and your domain expertise — knowing what a correct answer actually looks like — is far more valuable than technical knowledge at this stage.

Most teams don't — not rigorously. They rely on demos, a handful of manual checks, or user complaints after the fact. Lovelaice replaces that with a repeatable measurement process: you define what 'good' looks like for your specific use case, run your AI against a test set, and get a score you can track over time. When you change a prompt, swap a model, or update your data, you can see immediately whether quality improved or regressed — before it reaches users.

AI evals — short for evaluations — are structured tests that measure whether your AI products actually do what you think they do. Unlike traditional software tests with a clear pass or fail, AI outputs are probabilistic: the same prompt can return different results, and small changes can quietly break things users depend on. Without evals, teams judge quality by gut feel and anecdote. With them, you have numbers: accuracy rates, failure modes, regression signals. Lovelaice gives product and domain teams a way to run these evaluations against their own data — no data science background required.

Teams typically run their first meaningful experiment within a day and develop solid intuition within two to four weeks of regular use. The difference from traditional training programs is that the learning happens through your own work, not generic exercises. You're not studying prompt engineering in the abstract — you're finding out what actually works for your product, your users, and your data. That knowledge compounds fast.

The industry calls it 'vibe checking' — eyeballing a few outputs and deciding the model seems fine. The problem is it doesn't scale, it's inconsistent across team members, and it misses edge cases entirely. Lovelaice replaces vibe checks with structured evaluation: deterministic metrics for things you can measure precisely, and LLM-as-judge scoring for subjective quality dimensions. Once you've run your first eval, you have a baseline. Every subsequent experiment is measured against it.

Lovelaice gives you the output you need to make that case. Every experiment is logged with scores, comparisons, and outcome data. When you improve a model's accuracy from 60% to 85% on a key task, or cut response latency while maintaining quality, those are numbers you can put in front of a VP. Teams typically identify at least one significant quality improvement within the first two to three weeks of structured experimentation — improvements that would have taken months to surface through user complaints alone.

Yes. Regulated industries are actually where structured AI evaluation matters most, because 'it seemed to work in testing' isn't an acceptable compliance answer. Lovelaice gives compliance and domain experts direct access to AI evaluation — they can define quality criteria, review outputs, flag edge cases, and document their rationale. That audit trail is what regulators ask for. If your industry requires explainability and human oversight, systematic evaluation isn't optional: it's the product.