Neo - Autonomous AI Agent to build and evaluate AI models, AI Agents, LLM prompts and ML systems

Use Case 1: Automating Prompt Optimization

Problem: Manually testing dozens of prompt variations to find the most accurate result across different LLMs is time-consuming and subjective.
Solution: Neo uses an automated feedback loop where agents generate prompt iterations, test them against datasets, and track performance scores until the output converges on the best version.
Example: Refining a complex prompt for a legal document analyzer to ensure it consistently extracts structured data without missing key clauses.

Use Case 2: Developing and Evaluating RAG Pipelines

Problem: Setting up a Retrieval-Augmented Generation system requires choosing between various chunking methods and embedding models, which often requires extensive trial and error.
Solution: The platform's agents can independently build, test, and evaluate different RAG configurations to identify which combination yields the most relevant search results.
Example: A developer uses Neo to automatically benchmark five different vector retrieval strategies for an internal technical support bot.

Use Case 3: Debugging and Auditing ML Pipelines

Problem: Identifying subtle issues like data leakage or inefficient training sequences in a complex machine learning pipeline is difficult and prone to human error.
Solution: Neo analyzes existing project code to detect errors in the training sequence and identifies instances where future data may be leaking into the training set.
Example: Fixing a financial forecasting model training script that was showing suspiciously high accuracy due to data leakage from the validation set.