Company intelligence
Lovelaice
The AI experimentation platform built for product teams
About Lovelaice
Product analytics for AI features. Built for the product teams shipping AI in regulated industries. Most product teams shipping AI features are flying blind. They write a prompt, vibe-check three happy-path examples, ship, and then wait, for users to tell them something is wrong. Users don't tell them. They just stop using the feature. The team has no log, no signal, no idea the trust is gone. Lovelaice is what comes after the vibe check. We give product teams in FinTech, HealthTech, and LegalTech the same kind of analytics product teams have had since Amplitude and Mixpanel, for their AI features. You see what your AI actually does at scale. You catch silent failures before they cost you a renewal. You compare models, prompts, and instructions on your real data instead of generic LLM benchmarks. The teams using us go from 39.8% to 80% accuracy in 25 minutes. From 20% to 93% in one iteration. From "we tested it on a Slack thread" to a defensible business case in one afternoon. Domain experts run the platform. PMs, compliance leads, lawyers, clinical specialists. The people who actually know what "correct" looks like for your user. Available for evaluation audits, AI feature validation projects, and full-platform deployment.
Verified activity
Signals from Lovelaice
4 published signals
Presence & Recognition
Lovelaice is bringing its insights and experimentation framework to Circus Austin, TX, powered by Speero and Datadog, with a workshop on Monday, October 19 called Build your evals using AI experiments on a real product use case.
Reported by Lovelaice
Presence & Recognition
Lovelaice is hosting a lightning session on September 17 to walk through a complete eval suite on a real feature.
Reported by Madalina Turlea
Products & Services
Lovelaice has perfected what a full eval suite should look like in practice.
Reported by Madalina Turlea
Products & Services
Lovelaice is building AI coding agents to help teams identify outcomes and build products efficiently.
Reported by Madalina Turlea