Future AGI

Future AGI is an automated AI model evaluation platform that eliminates the need for manual QA evaluation by automatically scoring AI model output, allowing QA teams to focus on more strategic tasks, increasing efficiency and bandwidth by up to 10x. The platform uses natural language to define the metrics most important to the business, providing enhanced flexibility and control to evaluate model performance and ensure alignment with business goals. It also creates a cycle of continuous improvement by integrating performance data and user feedback into the development process, making the AI ​​smarter with every interaction.

Demand group:

"The target audience of Future AGI are businesses that seek to improve the accuracy and efficiency of AI models, especially those that have a large number of customer interactions and need to optimize AI models to improve service quality. It is suitable for companies that need to quickly iterate and optimize AI models to meet the needs of the business Targeted IT and Data Science Teams."

Example of usage scenario:

A large e-commerce platform uses Future AGI to optimize its recommendation system and improve user satisfaction and sales.

A financial services company used Future AGI to improve its risk assessment model and reduce credit losses.

A healthcare organization uses Future AGI to increase the accuracy of its diagnostic models, improving patient care.

Product features:

Automatic output scoring: Automatically evaluate AI model output without manual QA evaluation.

Custom Metrics: Use natural language to define the metrics that matter most to your business.

Continuous optimization: Integrate performance data and user feedback into the development process to achieve continuous optimization of AI models.

Interdisciplinary collaboration: Enable stakeholders from different fields to participate in the model optimization process.

Data Scientist Tools: Analyze and optimize AI models to ensure data-driven decision-making and performance evaluation.

Quality Assurance (QA) Engineer Tools: Rapidly test and validate AI systems to ensure performance and functionality after deployment.

AI Product Manager Tools: Monitor AI product performance, collect improvement insights, and align with business goals.

Private Cloud Deployment: Deploy in your own cloud with full control over your data and models.

Usage tutorial:

1. Register and log in to the Future AGI platform.

2. Define custom indicators based on business needs.

3. Upload the AI ​​model output data to the platform.

4. Use platform tools to evaluate model performance.

5. Adjust and optimize the AI ​​model based on the evaluation results.

6. Incorporate user feedback and performance data into model development.

7. Monitor model performance and iterate as needed.

8. Deploy the optimized AI model in the private cloud.