Teammately

What are the core features of Teammately?

• Prompt generation & self-refinement: Automatically selects models, generates prompts, and refines AI based on evaluation results.
• Test case & LLM Judge synthesizer: Creates high-quality test cases and metrics tailored to your AI project.
• Agentic RAG Builder: Handles chunking, embedding, and indexing for Retrieval-Augmented Generation (RAG).
• Interpretable AI Observability: Evaluates logs with multi-dimensional LLM judges to identify issues in production.
• AI-generated documentation: Updates documentation to reflect AI performance and team collaboration insights.

What are the benefits of using Teammately?

• Accelerates AI development: Reduces months of trial and error to deliver production-grade AI faster.
• Improves reliability: Automates evaluations and refinements to minimize AI failures and hallucinations.
• Simplifies RAG development: Handles complex processes like chunking and embedding automatically.
• Enhances team collaboration: Generates up-to-date documentation for clear performance tracking.
• Ensures scalability: Supports failover to secondary models and prompts for uninterrupted service.

What is the core purpose and selling point of Teammately?

• Purpose: To streamline AI engineering by automating iterative processes like prompt refinement and evaluation.
• Selling point: Makes AI resilient and production-ready faster than manual development.
• Unique value: Combines multi-dimensional LLM judges, RAG automation, and self-refining AI.
• Target users: AI engineers building LLM-based products, models, or agents.
• Key differentiator: Reduces tedious, expert-dependent work while maximizing safety and reliability.

What are typical use cases for Teammately?

• Developing AI services: Fast-tracks production-level AI for chatbots, assistants, or automation tools.
• Evaluating AI performance: Generates test cases and metrics to ensure quality and fairness.
• Building RAG systems: Automates retrieval-augmented workflows for knowledge-intensive applications.
• Monitoring AI in production: Identifies failures and edge cases with real-time observability.
• Team collaboration: Maintains synchronized documentation for AI projects across stakeholders.