Lumiflow offers an open‑source platform that transforms unstructured AI artifacts—such as model outputs, logs, and test cases—into interpretable signals, allowing product teams to define, compare, review, and improve AI behavior with domain expertise built into the evaluation loop. By moving evaluation beyond raw metrics, the tool lets non‑engineers make data‑driven product decisions and maintain reliable AI performance across releases.
Funding
Funding not disclosed
Founders
Product
Problem
AI product teams often rely on raw model metrics that are difficult for non‑engineers to interpret, leading to decisions based on opaque scores rather than concrete evidence of behavior. This makes it hard to incorporate domain expertise into the evaluation loop and to reliably improve AI performance across diverse applications.
Solution
Lumiflow offers open‑source workflows that convert unstructured AI artifacts—such as logs, outputs, and user interactions—into structured, interpretable signals. These signals enable product teams to define evaluation criteria, compare model versions, and review behavior with domain experts directly involved. By embedding expert judgments into the evaluation process, the platform turns qualitative insights into quantitative evidence that can guide product decisions. The system is designed for non‑engineers, providing a visual and collaborative interface that abstracts away low‑level model details. Because the workflows are open source, teams can customize and extend them to fit specific domain requirements while maintaining transparency and reproducibility.
Target Audience
Primary users are AI product managers, product teams, and domain experts who need to assess and improve model behavior without deep engineering expertise.
Features
- Open‑source pipeline that ingests raw AI artifacts and produces standardized evaluation metrics
- Visual dashboards that present interpretable signals for easy review by product managers and domain experts
- Collaboration tools that allow experts to annotate, rank, and provide feedback on model behavior within the workflow
- Version comparison utilities to track changes in AI performance across releases
- Extensible framework supporting custom data types and domain‑specific evaluation criteria
- Integration hooks for common AI development environments and data storage solutions