Refiant offers a high‑capacity inference platform that lets developers work with up to 10 million tokens of context, available via an API or private deployment for data‑sovereign environments. By combining compression and context‑management techniques, it reduces compute while preserving relevant information, enabling use cases such as long‑term messaging archives, decade‑spanning document analysis, and 24/7 AI agents that operate on real‑time data. The service is designed for teams that need to reason over extensive, complex datasets in a single session.
Funding
Funding not disclosed
Founders
Product
Problem
Many AI applications require processing extremely long documents, message histories, or continuous data streams, but existing inference platforms are limited to short context windows, leading to fragmented analysis and high compute costs. This limitation hampers use cases such as compliance review, long‑term research, and 24/7 autonomous agents that need to reason over years of information.
Solution
Refiant offers an AI inference platform that supports context windows of up to 10 million tokens via both API and chat interfaces. The service combines token compression with dynamic context management to surface only the most relevant information, reducing unnecessary compute while preserving the ability to reason over extensive data. Private deployment options let organizations run the platform in their own environment, ensuring data sovereignty for sensitive workloads. The platform enables continuous AI agents to access years of messaging, documentation, and real‑time inputs within a single session, facilitating compliance checks, complex research queries, and always‑on agentic workflows.
Target Audience
Primary customers are enterprises and research teams that need to analyze large corpora of text—such as compliance departments, legal teams, and organizations building continuous AI agents—where data privacy and long‑term context are critical.
Features
- Context windows up to 10 million tokens for single‑pass inference
- Compression and relevance‑based context management to optimize compute usage
- API and chat interfaces for flexible integration into existing pipelines
- Private deployment capability for on‑premise or isolated cloud environments, preserving data sovereignty
- Support for long‑term data sources such as 5‑year message archives and 10‑year document collections
- Real‑time data ingestion enabling 24/7/365 autonomous agents with persistent context