Provides a platform for building, testing, and versioning large language model (LLM) deployments, enabling teams to create complex model chains, automate evaluations, and maintain prompt versioning. It streamlines LLM development by integrating tools for analytics, observability, and API key management, ensuring efficient iteration and collaboration across technical and non-technical stakeholders.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Developing and deploying large language model (LLM) applications involves complex workflows for prompt engineering, model evaluation, and version control. Teams often lack integrated tools to streamline these processes, leading to inefficiencies in iteration, collaboration, and ensuring model quality.
Solution
Chatter is a platform designed to streamline the entire LLM application lifecycle, providing tools for building, testing, and versioning LLM deployments. The platform enables users to construct complex model chains with function calling, data manipulation, and Retrieval Augmented Generation (RAG) capabilities. It automates model evaluations using various metrics, including LLM-based evaluation and semantic similarity, and maintains prompt versioning for reproducibility. Chatter facilitates collaboration between technical and non-technical stakeholders through a shared interface with analytics, observability, and API key management features.
Target Audience
Chatter targets LLM application developers, data scientists, and product teams building AI-powered products who need a comprehensive platform for managing the LLM development lifecycle.
Features
- Visual interface for building complex LLM chains with function calling and data manipulation.
- Automated evaluation suite with metrics like LLM-based evaluation, semantic similarity, and regex matching.
- Prompt versioning and logging for tracking changes and ensuring reproducibility.
- Jinja2 templating engine for intermediate data transformations within prompts.
- Integrated RAG pipeline setup with vector database support.
- API key vault for managing LLM API keys and tracking token usage.
- Analytics dashboard providing insights into call duration, token usage, cost, and performance.
- Observability tools for debugging complex LLM chains.
- Function builder for creating and maintaining a library of function calls.
- Routing capabilities for complex flows involving multiple functions and system prompts.
- Chat testing functionality with support for multiple roles and seamless chat import.
- SDK & Code Export to separate iteration from codebase.