FAQ from Dioptra
What is Dioptra?
Dioptra is an open-source, data-native platform designed to accelerate AI development by putting data quality, traceability, and intelligence at the center—supporting CV, NLP, and LLM teams in curating smarter datasets, diagnosing failures at the data layer, and closing the loop between insight and action.
How to use Dioptra?
Start by ingesting raw or partially labeled data, then leverage Dioptra’s interactive dashboard and CLI to score, inspect, and triage samples. Register metadata to build searchable data catalogs, run diagnostic reports to surface failure cohorts, trigger active learning cycles, and export refined datasets directly into your labeling or training systems.
How does Dioptra work?
Dioptra operates as a modular, extensible data operations layer: it ingests model outputs and raw data, computes actionable signals (e.g., prediction entropy, embedding outliers), surfaces insights via visual analytics, and exports prioritized subsets—enabling engineers to make precise, evidence-based curation decisions—not guesswork.
What are the core features of Dioptra?
At its core, Dioptra delivers intelligent data curation, schema-flexible metadata management, failure-aware diagnostics, production-ready active learning, and frictionless integration—all unified under an Apache 2.0 open-source license.
What are some use cases for Dioptra?
Teams use Dioptra to harden safety-critical models (e.g., detecting adversarial examples in LLMs), scale annotation for multilingual NLP, improve generalization in long-tail CV tasks, and establish auditable data governance for regulated AI deployments.
Is pricing information available?
Dioptra is 100% open source and free to use. Optional enterprise support, managed cloud hosting, and custom integrations are available—visit dioptra.ai/pricing for details.