Athina is a collaborative platform for building, testing, and monitoring AI features, enabling teams to ship models to production faster. It provides tools for prompt management, dataset evaluation using preset and custom evals, and programmatic flow prototyping. The platform offers native LLM observability, tracing, and continuous online evaluations to ensure model reliability in production environments.
Funding
$4.1M 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.
ARDYFounders
Product
Problem
Developing and deploying AI features requires rapid prototyping, robust testing, and continuous monitoring, which can be challenging for both technical and non-technical teams. Existing workflows often lack streamlined collaboration and customizable evaluation metrics, hindering the efficient development of reliable AI applications.
Solution
Athina is a collaborative AI development platform designed to streamline the process of building, testing, and monitoring AI features. It provides a centralized environment where teams can manage prompts, evaluate datasets, and conduct experiments using customizable metrics. The platform supports both programmatic access for engineers and a user-friendly interface for non-technical users, fostering collaboration and accelerating AI development cycles. Athina offers tools for prompt engineering, dataset management, continuous evaluation, and real-time monitoring, enabling teams to build and deploy production-grade AI applications with greater confidence.
Target Audience
Athina is designed for AI product teams, data scientists, product managers, QA teams, and engineers who need a collaborative platform to build, test, and monitor AI applications efficiently.
Features
- Prompt management with version control, A/B testing, and the ability to run prompts against different models
- Dataset evaluation using over 50 preset evaluation metrics, with the option to configure custom evaluations using LLMs, Python functions, or external APIs
- Experimentation tools for regenerating datasets by modifying models, prompts, or retrievers
- Annotation capabilities for human verification of evaluation results and dataset labeling, including inter-annotator agreement tracking
- Flow builder for chaining prompts, API calls, retrievals, and code functions into complex AI pipelines
- Real-time monitoring of LLM traces, usage metrics, and evaluation scores in production
- Integration with custom models hosted on platforms like Azure OpenAI and AWS Bedrock
- Role-based access control and self-hosted deployment options for enhanced data privacy and security, including SOC-2 Type 2 compliance