ChatBetter

ChatBetter was an AI product from Unsupervised focused on improving LLM responses through multi-model orchestration. The official ChatBetter site describes the project as research into routing questions to the right model, grounding answers in source data, and using agents to evaluate and combine outputs.

What ChatBetter does

ChatBetter’s approach was to treat answer quality as an orchestration problem rather than relying on one language model for every request. According to the team, the system explored how to select an appropriate model for a task, connect responses to source data, and apply automated judgment across outputs. The stated goal was to support more trustworthy AI output through routing, grounding, and quality guardrails.

The site currently presents ChatBetter primarily as a completed learning phase from Unsupervised. It says that the team spent billions of tokens studying multi-model orchestration, quality controls, and reliable outputs, and that these findings are informing newer products. It does not provide current pricing, an active feature list, or product availability details.

Who it helps

ChatBetter is most relevant to people evaluating ways to improve LLM answer quality across multiple models. This includes practitioners interested in model routing, source-grounded responses, automated output evaluation, and agent-based workflows. The available information does not identify specific industries, integrations, plans, or supported models.

Notable capabilities

• Question routing: Directing questions to a model suited to the task.
• Source grounding: Using source data to ground responses.
• Automated evaluation: Letting agents judge and combine model outputs.
• Quality guardrails: Exploring controls intended to support more trustworthy AI output.

How it fits a workflow

The described workflow starts with a user question, routes it toward an appropriate model, grounds the response in source material, and uses agents to assess or combine the resulting outputs. This makes ChatBetter’s contribution primarily an orchestration layer for LLM workflows rather than a single-model chatbot description.

Strengths and limits

Its clearest strength is a focus on combining model selection, grounding, and automated judging instead of treating one model’s first answer as sufficient. However, the current official page is retrospective and focuses on what Unsupervised learned. Because it does not document current access, pricing, integrations, model support, or measurable performance, those details should be confirmed before treating ChatBetter as an available production tool. The page instead highlights newer work from Unsupervised, including long-running data analysis and process automation, along with open-source projects inspired by ChatBetter.