Dottxt provides an API for machine learning models that enhances information extraction and classification by implementing guardrails to ensure predictable outputs. This approach addresses the inherent uncertainty in large language models, enabling their reliable integration into software applications.
Funding
$12.2M 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
Large language models (LLMs) are inherently probabilistic, leading to unpredictable outputs that hinder their reliable integration into software applications. This uncertainty makes it difficult for developers to use LLMs in systems requiring consistent and structured responses.
Solution
Dottxt provides an API that enhances information extraction and classification from LLMs by implementing guardrails to ensure predictable outputs. By applying statistical modeling techniques, Dottxt constrains LLMs to generate responses that adhere to predefined rules and formats. This approach reduces errors and uncertainty, enabling developers to reliably integrate LLMs into their applications. Dottxt allows developers to design, execute, deploy, and evaluate LLM applications with greater control and consistency.
Target Audience
Dottxt targets developers and organizations seeking to integrate LLMs into their software applications but require more predictable and reliable outputs.
Features
- API for implementing guardrails on LLM outputs
- Statistical modeling techniques to constrain LLM responses
- Predictable and structured outputs for reliable integration
- Customizable rules and formats based on specific criteria
- Reduced errors and uncertainty in LLM-generated content
- Tools for designing, executing, deploying, and evaluating LLM applications