Fastino provides small, purpose‑optimized language models for tasks such as named‑entity recognition, privacy filtering, and content moderation. Its open‑source models (e.g., GLiNER, GLiGuard, GLiNER2‑PII) and the Pioneer agent platform automate fine‑tuning and inference, offering low‑latency REST APIs that reduce compute costs and simplify integration for AI teams building production extraction and safety pipelines.
Funding
$7M 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.
Founders
Product
Problem
Many organizations struggle to deploy large language models for specialized tasks such as entity extraction, privacy filtering, and safety moderation because these models are resource‑intensive, costly to fine‑tune, and often produce sub‑optimal performance on domain‑specific data.
Solution
Fastino builds and offers small, purpose‑optimized language models that deliver high accuracy on structured data tasks while requiring far less compute than full‑scale LLMs. The company provides open‑source models (e.g., GLiNER for named‑entity recognition, GLiGuard for content moderation, GLiNER2‑PII for privacy filtering) together with the Pioneer agent platform that automates fine‑tuning and inference of both open‑source and proprietary models. Fastino’s models are accessible via REST APIs, enabling developers to integrate extraction, classification, and safety pipelines directly into production workflows. By focusing on task‑specific architectures and efficient inference, Fastino reduces latency and operating costs for AI applications that need reliable, domain‑aligned language understanding.
Target Audience
Primary customers are AI engineering teams, data scientists, and product developers at enterprises and startups that need efficient, task‑specific language models for structured data extraction, privacy compliance, and safety moderation.
Features
- GLiNER series: small transformer models delivering state‑of‑the‑art named‑entity recognition across multiple languages with schema‑driven interfaces
- GLiGuard: schema‑conditioned classification model for fast, accurate content moderation and safety filtering
- GLiNER2‑PII: 300 M‑parameter multilingual model for detecting and redacting personally identifiable information
- Pioneer agent: automated pipeline for continual fine‑tuning, versioning, and inference of small language models in production environments
- Low‑latency REST API endpoints for extraction, classification, and moderation tasks, designed for easy integration into existing services
- Open‑source releases with permissive licenses, allowing customization and on‑premise deployment
- Community support via Discord and extensive documentation to accelerate adoption and troubleshooting