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What is Future AGI?

Leverage our automated insights and customizable metrics to evaluate, improve, and continuously refine your GenAI models. Future AGI simplifies the process of assessing AI model outputs by automatically scoring them, which eliminates the need for manual quality assurance checks. Consequently, your QA team can focus their efforts on more strategic initiatives, potentially increasing their efficiency and capacity by as much as tenfold. This guarantees that interactions driven by AI remain consistently positive and in line with your brand identity. By optimizing your models, you can showcase the most relevant and engaging content tailored for each individual user. Furthermore, you have the ability to fine-tune your models to generate the most accurate summaries for your target audience. Future AGI enables you to create custom metrics that measure your AI model's accuracy based on the unique priorities of your specific use case. You can express your critical metrics in natural language, granting your QA team enhanced flexibility and authority in evaluating model performance. This approach ensures that your evaluations align with your business objectives, moving beyond traditional metrics like relevance to support a more thorough assessment framework. Embracing this strategy not only improves model performance but also cultivates a culture of ongoing enhancement within your organization. Ultimately, this commitment to refining your AI capabilities will significantly elevate the overall user experience and drive better outcomes for your business.

What is Arize Phoenix?

Phoenix is an open-source library designed to improve observability for experimentation, evaluation, and troubleshooting. It enables AI engineers and data scientists to quickly visualize information, evaluate performance, pinpoint problems, and export data for further development. Created by Arize AI, the team behind a prominent AI observability platform, along with a committed group of core contributors, Phoenix integrates effortlessly with OpenTelemetry and OpenInference instrumentation. The main package for Phoenix is called arize-phoenix, which includes a variety of helper packages customized for different requirements. Our semantic layer is crafted to incorporate LLM telemetry within OpenTelemetry, enabling the automatic instrumentation of commonly used packages. This versatile library facilitates tracing for AI applications, providing options for both manual instrumentation and seamless integration with platforms like LlamaIndex, Langchain, and OpenAI. LLM tracing offers a detailed overview of the pathways traversed by requests as they move through the various stages or components of an LLM application, ensuring thorough observability. This functionality is vital for refining AI workflows, boosting efficiency, and ultimately elevating overall system performance while empowering teams to make data-driven decisions.

Media

Media

Integrations Supported

Gemini Enterprise Agent Platform
OpenAI
Hugging Face

Integrations Supported

Gemini Enterprise Agent Platform
OpenAI
APIFuzzer
Arize AI
Codestral Mamba
Conda
CrewAI
Databricks
Haystack
JavaScript
JupyterLab
LangChain
Mistral 7B
Mistral AI
Mixtral 8x22B
Mixtral 8x7B
Pixtral Large
Slack
Vercel

API Availability

API Availability

Has API

Pricing Information

Pricing not provided
Free Version

Pricing Information

Free
Free Version

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Future AGI

Date Founded

2024

Company Location

United States

Company Website

www.futureagi.com

Company Facts

Organization Name

Arize AI

Company Location

United States

Company Website

docs.arize.com/phoenix

Categories and Features

Agentic AI

Not specified

Categories and Features

AI Observability

Not specified

LLM Evaluation

Not specified

Semantic Layer

Not specified

Telemetry

Not specified

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