What does Phoenix AI do?

Phoenix AI is a customer service platform powered by artificial intelligence that automates conversations, responses, and customer support tasks. It combines natural language understanding with task automation to respond to inquiries, take action, and summarize conversations.

What are the key features?

Phoenix AI includes automated dialogues that suggest AI-generated responses, task execution capabilities that create and update support tickets from chat interactions, action item summaries after each engagement, fast bot setup in seconds, custom workflows through API integrations, and supervised human review to ensure quality responses.

Who should use Phoenix AI?

Customer support teams automating FAQs, e-commerce platforms providing 24/7 support, SaaS businesses automating support workflows, startups and SMBs scaling support without hiring, and enterprises managing multiple customer interaction channels can all benefit from Phoenix AI.

What are the advantages and disadvantages?

The advantages of Phoenix AI include reducing manual customer response time, multi-platform compatibility, faster time-to-market, human-in-the-loop monitoring, and the ability to scale support without additional hiring. The disadvantages include requiring domain-specific training, developer time for unique integrations, occasional context-related AI errors, some features being limited to low-tier plans, and performance depending on training data quality.

What is PhoenixAI at UChicago?

PhoenixAI is a chat interface built on Microsoft's Azure platform using OpenAI's GPT models. As of October 14, 2025, users can choose between three models: GPT-5.0, GPT-4.1, and o3.

What models are available?

PhoenixAI offers GPT-5.0, GPT-4.1, and o3 models, with Assistants only available with the GPT-4.1 model. Users can create and share custom Assistants as of November 2024.

How long do sessions last?

Each PhoenixAI session remains active for 1 hour with a 30-minute idle timeout. If you're inactive for over 30 minutes, your session automatically ends and you'll need to refresh or start a new session.

Is my data secure and private?

Data submitted to PhoenixAI is kept within the UChicago environment and not shared with third-party vendors or used to train AI models. You can choose to have PhoenixAI retain or delete your prompt data after each session.

What are PhoenixAI Assistants?

Assistants are shareable AI tools designed to handle unstructured data and text-based inputs. They use Retrieval Augmented Generation (RAG) to search uploaded files for relevant information. You can upload up to 10 files at once (maximum 100 files per Assistant), with a 20MB limit per file and 3,000 character limit for instructions.

Do Assistants learn from my data?

No, Assistants do not learn from your prompts, uploaded files, or any of your data, maintaining your privacy and data security.

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Phoenix

4.6

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Phoenix is an open-source tool for AI developers to trace, evaluate, and optimize their Large Language Models (LLMs). It provides tools to understand how your AI makes decisions, experiment with prompts, and identify problems like incorrect outputs, helping you build more reliable and effective AI applications faster.

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About Phoenix

Who It's For

Phoenix is for AI developers and engineers building with Large Language Models (LLMs). It helps you understand, test, and improve your AI applications. This open-source tool offers full control to debug and optimize your AI performance.

What You Get

You get clear tracing of your AI's decisions and an interactive prompt playground to test ideas. Phoenix also provides tools to evaluate AI responses and incorporate human feedback. It helps you group data to spot performance issues easily.

How It Works

Phoenix sets up easily using OpenTelemetry, collecting data from your AI applications. It lets you trace AI decisions, experiment with prompts, and evaluate responses. This helps you quickly find and fix problems, such as when your AI gives incorrect or misleading information.

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Features & Capabilities

📊 LLM Observability & Debugging

Application Tracing

Provides total visibility into LLM application data with automatic or manual instrumentation.

Dataset Clustering & Visualization

Uncovers semantically similar questions, document chunks, and responses using embeddings to isolate poor performance.

Real-time LLM Evaluation

Enables evaluation, experimentation, and optimization of AI products in real time.

Model Interpretability

Visualizes complex LLM decision-making and flags when and where models fail or produce poor responses.

🧪 Prompt Engineering & Evaluation

Interactive Prompt Playground

Offers a fast, flexible sandbox for prompt and model iteration, output visualization, and debugging.

Streamlined Evaluations

Leverages a high-speed evaluation library with pre-built, customizable templates for any task.

Human Feedback Integration

Incorporates human feedback into the evaluation process to enhance model assessment.

⚙️ Platform Architecture & Integration

OpenTelemetry-based Foundation

Uses OpenTelemetry for seamless setup, full transparency, and freedom from vendor lock-in.

Open Source & Self-Hostable

Provided as a fully open-source solution, allowing for self-hosting without feature restrictions.

Broad LLM Tool Compatibility

Works with all major LLM tools and frameworks, supporting easy integration into existing workflows.

Use Cases

Accelerating LLM Application Debugging and Iteration

AI engineers face challenges in quickly identifying root causes of poor LLM performance, such as hallucinations or irrelevant responses, during development. Phoenix provides interactive tracing for total visibility, an interactive prompt playground for rapid experimentation, and visual dataset clustering to efficiently debug and iterate on prompts and models, significantly improving application quality and development speed.

AI/ML DevelopmentFor: AI Engineers

Ensuring Production LLM Performance and Observability

For LLM applications in production, it's crucial to continuously monitor performance, detect regressions, and ensure ongoing reliability. Phoenix enables streamlined evaluations with customizable templates, incorporates human feedback, and provides extensive observability utilities to proactively track model behavior, identify performance degradation, and manage the entire LLM lifecycle effectively.

MLOpsFor: MLOps Engineers

Deep Root Cause Analysis for Complex LLM Behaviors

When LLM applications generate unexpected or problematic outputs like hallucinations or problematic responses, data scientists need deep visibility to diagnose the issue. Phoenix offers application tracing to visualize complex LLM decision-making workflows and dataset clustering to uncover semantically similar inputs or outputs, enabling precise identification and resolution of failure points related to retrieval and tool execution.

Data ScienceFor: Data Scientists

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Overview Features Use Cases FAQ Integrations Pricing