Swept AI provides an enterprise framework for AI trust, safety, and compliance across any model or cloud environment. The platform enables teams to evaluate AI models before launch, supervise performance in production, and generate human-readable proof for stakeholders. This unified approach accelerates decision-making while ensuring ongoing quality control and verifiable evidence for risk management.
Funding
Funding not disclosed
Founders
Product
Problem
AI models can be unpredictable and often harbor hidden risks related to performance, scalability, compliance, and security. Without proper monitoring, these risks can lead to costly model failures, legal issues, reputational damage, and erosion of trust. Currently, a small percentage of AI projects reach the release phase due to these challenges.
Solution
Swept AI offers an AI performance monitoring platform designed to detect anomalies, biases, and security risks in AI agent behavior, both before and after deployment. The platform employs statistical frameworks and quantitative analysis to evaluate AI performance, ensuring reliability and oversight. By identifying potential failure points and providing continuous monitoring, Swept AI helps organizations validate AI applications, supervise agent behavior, and prevent issues like drift and bias from impacting users. This allows companies to deploy AI with greater confidence, mitigate risks, and maintain the integrity of their AI applications.
Target Audience
The primary target audience includes software companies, AI developers, and organizations deploying AI systems in high-risk or high-volume environments.
Features
- Risk assessment and discovery to identify hidden risks in performance, scalability, and compliance.
- Prioritization of critical failures, performance bottlenecks, and decision-making blind spots.
- Refinement, stress-testing, and validation of AI prototypes to accelerate time to market.
- Implementation of solutions to improve model performance, security, and compliance.
- Ongoing monitoring, compliance validation, and performance assessments to stay ahead of new vulnerabilities.
- Customizable alerts for real-time monitoring and rapid response to deviations in AI behavior.
- Proprietary criteria to continuously enhance AI performance and drive better outcomes.