AI Astrolabe supplies human‑curated, high‑quality training, evaluation, and safety datasets for low‑resource languages, covering text, audio, and image modalities. Their offerings include supervised fine‑tuning data, culturally aligned preference sets, and adversarial red‑team data created by native‑speaker experts to ensure dialect nuance and safety across diverse linguistic contexts. Teams can start with a pilot dataset and scale to production volumes, integrating the data directly into their AI pipelines.
Funding
Funding not disclosed
Founders
Product
Problem
AI developers often lack high-quality, human‑curated datasets for low‑resource languages, making it difficult to train models that understand local dialects, cultural nuances, and multimodal content. Without reliable training, evaluation, and safety data, AI systems can produce inaccurate or culturally insensitive outputs, limiting their usefulness in many regions.
Solution
AI Astrolabe provides curated datasets and evaluation assets specifically for low‑resource languages. Their offerings include supervised fine‑tuning data, preference datasets, and multimodal content (images with captions, audio, and visual question‑answer pairs) all created and peer‑reviewed by native‑speaking experts. They also deliver adversarial red‑team testing and safety‑focused data to identify edge‑case failures before deployment. Clients can start with a pilot dataset to validate quality and then scale to larger production volumes, integrating the data directly into their AI pipelines.
Target Audience
AI Astrolabe serves machine‑learning teams building language models, multimodal systems, or safety‑critical applications that need reliable data for low‑resource languages and dialects.
Features
- Supervised fine‑tuning and preference data across multiple dialects, domains, and modalities, authored by native speakers
- Captioned and transcribed image datasets, as well as audio and visual question‑answer benchmarks for multimodal training
- Adversarial red‑team datasets that stress‑test model safety and uncover edge‑case failures in low‑resource languages
- Expert peer‑review process ensuring dialect verification, factual accuracy, and cultural sensitivity for every sample
- Intrinsic and extrinsic quality metrics, including linguistic diversity scoring and bias/robustness assessments
- Pilot‑to‑production workflow that lets teams validate data quality on a small scale before scaling up