Autumn8 provides an AI Compliance OS that continuously analyzes enterprise policies, code, and cloud systems against established compliance frameworks. The platform generates real-time evidence, identifies gaps, and suggests fixes to maintain continuous alignment between requirements and operations. This solution automates evidence collection and remediation, streamlining GRC workflows and reducing manual effort associated with audits and security questionnaires.
Funding
Funding not disclosed
Founders
Product
Problem
Selecting and deploying the optimal generative AI model for a specific application is a time-consuming and complex task, given the rapidly increasing number of available models. Ensuring compliance with performance requirements and navigating the complexities of deployment across various cloud platforms further compounds the challenge. Additionally, enterprises face difficulties in efficiently assessing and maintaining compliance for their GenAI applications, often requiring significant time and resources.
Solution
Autumn8 offers a suite of tools designed to streamline the selection, deployment, and management of generative AI models, while also addressing compliance challenges. The Model Selector analyzes model capabilities based on input data, architecture, and other parameters to identify the best fit for a given application. The Model Server simplifies DevOps automation by packaging, deploying, and managing trained AI models or containers. The Inference Optimizer enhances performance through workload, system, and server-level optimizations, supporting various model types across multiple cloud platforms and on-premise environments. Autumn8 also provides a Compliance Assessor that automates the preparation of GenAI compliance reports, significantly reducing the time required for assessment and increasing approval rates.
Target Audience
Autumn8 targets enterprises and developers seeking to efficiently select, deploy, manage, and maintain compliance for generative AI models and applications across various cloud and on-premise environments.
Features
- **Model Selector:** Analyzes model capabilities using input data, model architecture, and other parameters to recommend the optimal model for a specific application.
- **Model Server:** Automates DevOps by packaging, deploying, and managing trained AI models or containers.
- **Inference Optimizer:** Improves performance through workload, system, and server-level optimizations, supporting foundation, deep learning, and ML models.
- **SLA Predictor:** Estimates AI model performance across cloud instances and platforms, recommending instances based on latency, throughput, cost, emissions, and energy consumption.
- **Compliance Assessor:** Auto-fills compliance assessment questions using an LLM to search, comprehend, and structure product and model documents.
- **Project Management Module:** Builds a library of LLM-processed documents across multiple projects and clients to save time and resources.
- **Legal Module:** Provides an interface for legal teams to assess compliance sufficiency and identify areas for further documentation, with track and trace features linking back to originating documents.