CoSupport AI
What is Future AGI?
Future AGI provides a comprehensive platform for enterprises seeking to develop trustworthy and accurate AI applications. It offers tools to evaluate and optimize AI performance across both software and hardware environments. The platform aims to enhance the reliability of AI systems by enabling thorough assessment and improvement cycles.
The system facilitates the generation and management of diverse synthetic datasets for effective AI model training and testing, including handling edge cases. It allows users to experiment with, compare, and analyze various agentic workflow configurations using built-in or custom metrics without requiring code. Furthermore, Future AGI enables performance assessment, root cause analysis, and provides actionable feedback for improvement, alongside real-time monitoring and protection features for applications in production, including safety metrics to block unsafe content.
Features
- Synthetic Dataset Generation: Generate and manage diverse synthetic datasets to effectively train and test AI models, including edge cases.
- Agentic Workflow Experimentation: Test, compare and analyse multiple agentic workflow configurations to identify the ‘Winner’ based on built-in or custom evaluation metrics without code.
- Performance Evaluation & Root Cause Analysis: Assess and measure agent performance, pin-point root cause and close loop with actionable feedback using proprietary eval metrics.
- AI Performance Improvement: Enhance LLM application performance by incorporating feedback from evaluations or custom input, with automatic prompt refinement.
- Production Monitoring & Protection: Track applications in production with real-time insights, diagnose issues, improve robustness, and block unsafe content using safety metrics.
- Multimodal Evaluation: Evaluate AI across different modalities including text, image, audio, and video, pinpoint errors, and receive feedback for improvement.
- Seamless Integration: Integrates with industry-standard tools allowing teams to maintain existing workflows.
Use Cases
- Evaluating and optimizing AI model accuracy.
- Improving LLM application performance.
- Generating synthetic data for AI training and testing.
- Monitoring AI applications in production.
- Ensuring AI safety and responsibility.
- Streamlining retail analytics with enhanced SQL accuracy.
- Enhancing meeting summarization quality.
- Improving AI-driven lead generation response rates.
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