Lovelace provides an AI experimentation platform designed specifically for product teams to test and validate large language models against proprietary data. This platform enables product managers and domain experts to run blind evaluations across numerous models and prompt variations without relying on engineering resources. The result is a documented, data-driven AI playbook that accelerates deployment and optimizes model selection based on real-world performance and cost analysis.
Funding
$16.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
RVFounders
Product
Problem
Product teams often lack the expertise and resources to evaluate large language models (LLMs) against their own use cases, forcing them to rely on engineering bottlenecks or generic benchmarks. This results in guesswork, prolonged iteration cycles, and uncertainty about model cost and performance. Consequently, valuable AI initiatives stall or are deployed without validated evidence.
Solution
Lovelace offers an AI experimentation platform tailored for product managers and domain experts to run prompt tests across more than 15 LLMs using their proprietary data. Users can upload real‑world queries, documents, or data samples and conduct blind, side‑by‑side evaluations without writing code or involving engineers. The system automatically analyzes results, highlighting which models excel on specific tasks, their failure modes, and cost implications. All experiment outcomes are captured in a searchable knowledge base that forms a reusable AI playbook for the organization. When a configuration meets the desired criteria, Lovelace generates production‑ready specifications that engineering teams can implement directly. The platform thus compresses weeks of trial‑and‑error into days, reduces reliance on engineering, and provides clear ROI projections before any deployment.
Target Audience
The primary users are product managers, domain experts, and product leadership in technology‑driven companies who need to evaluate LLMs without extensive engineering effort, as well as engineering teams that consume validated model configurations for production deployment.
Features
- Upload and manage domain‑specific test cases (customer queries, documents, data samples) for realistic evaluation
- Simultaneous experimentation on 15+ LLMs with configurable prompts, parameters, and temperature settings
- Blind evaluation mode with side‑by‑side performance metrics (accuracy, latency, token cost) across models
- Automated analysis engine that surfaces patterns, model strengths/weaknesses, and cost‑benefit recommendations
- Centralized, searchable repository that records prompts, configurations, results, and insights to build an AI playbook
- One‑click export of validated configurations with clear deployment requirements and integration hooks for production systems
- Role‑based access controls enabling product managers, domain experts, and engineering teams to collaborate within a single source of truth
- Built‑in ROI calculator that projects model costs versus accuracy gains for informed budgeting decisions