Radiant AI focuses on operationalizing artificial intelligence solutions for businesses. The platform enables organizations to integrate and manage complex AI models within their existing workflows. This service ensures that deployed AI delivers measurable business outcomes efficiently.
Funding
$3.5M 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
Enterprises face challenges in deploying and managing generative AI applications, including difficulties in controlling model access, ensuring consistent performance, and maintaining security across diverse use cases. Monitoring model behavior, identifying outliers, and aligning performance with business outcomes requires complex configurations and integrations.
Solution
Radiant AI offers a platform designed to streamline the deployment, management, and alignment of generative AI applications. The platform provides tools for real-time monitoring of LLM activity, integration of external KPIs, and incorporation of human feedback to refine model outputs. Its model gateway enables intelligent routing, access controls, and budget management across different models, applications, data sources, and users. Radiant AI helps businesses ensure high-quality, relevant AI experiences by facilitating rapid characterization of user interactions and model responses through evaluators, filters, and clustering.
Target Audience
Radiant AI targets enterprises seeking to deploy and manage generative AI applications, including those focused on customer support, RAG (Retrieval-Augmented Generation) and vector search, code generation, AI agents, and summarization.
Features
- Real-time monitoring of LLM activity across various models and use cases
- Integration of external KPIs to correlate model performance with business outcomes
- Human feedback incorporation for categorizing results and validating evaluations
- Comparison testing to measure the impact of changes before production deployment
- Custom evaluators for configuring evaluations specific to use cases or integrating external evaluation tools
- Outlier detection to identify subtle anomalies in large datasets
- Alerting framework for setting conditions to detect specific behaviors and synchronizing with observability tools
- Intelligent routing to manage traffic between applications and LLMs
- Access controls for setting fine-grained permissions synchronized with identity management systems
- Budgeting and tagging for monitoring spend and setting limits across use cases, customers, and users