Teammately

Teammately is an AI agent platform designed for AI engineers to build production-level AI. It automates and accelerates the process of evaluation, iteration, and execution to make AI systems more reliable and harder to fail. The platform provides features for prompt generation and self-refinement, test case and LLM judge synthesis for evaluation, an agentic RAG builder, interpretable AI observability, and AI-generated documentation. Teammately aims to minimize hallucinations and edge cases by automating tasks such as selecting foundation models, generating prompts, synthesizing test cases, cleaning data, and comparing multiple AI architectures. It also supports multi-dimensional LLM judge evaluation, failover to secondary models and prompts, and centralized management of models, prompts, and retrieval engines with one-click switching.

Key Features

• Prompt Generation & Self-refinement: Automatically selects models, generates prompts, and refines based on evaluation results.
• Test Case & LLM Judge Synthesizer: Generates test cases and evaluation metrics aligned with project requirements.
• Agentic RAG Builder: Automates chunking, embedding, and indexing for retrieval-augmented generation.
• Interpretable AI Observability: Evaluates logs with multi-dimensional LLM judges to identify problems in production.
• AI-Generated Documentation: Creates and updates documentation showing AI performance and challenges.
• Multi-dimensional LLM Judge: Supports 3-grade, pairwise, and voting evaluation methods.
• Multi-architecture Evaluation: Compares Prompt, RAG, and model architectures simultaneously.
• Failover to Secondary Models & Prompts: Automatically routes to backup models with tailored prompts.
• Centralized Management: Containerizes models, prompts, and retrieval engines for easy switching and rollback.

Key Benefits

• Automates prompt generation and self-refinement
• Generates test cases and evaluation metrics automatically
• Provides multi-dimensional LLM judge for reliable evaluation
• Enables one-click switching between models, prompts, and databases