Everest provides an agentic AI platform specifically designed for life sciences organizations to generate complex regulatory, clinical, and strategic documents rapidly. The platform automates the creation of essential deliverables like IND/CTA packages and Clinical Study Reports, ensuring accuracy and compliance. This enables pharmaceutical, biotech, and MedTech teams to transform insights into scalable, automated workflows.
Funding
$7.2M 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) can produce unreliable outputs, leading to errors and hallucinations, especially in high-stakes domains. Measuring the subjective quality of LLM outputs and ensuring accuracy in real-time is challenging, often resulting in poor user experiences. Scaling expert oversight to review the massive volume of LLM completions is also difficult and creates a bottleneck.
Solution
Log10 offers an LLMOps platform designed to enhance the accuracy and reliability of LLM applications. The platform provides tools for real-time monitoring, automated feedback, and expert evaluation, streamlining the process of improving LLM accuracy. It enables users to create evaluation criteria, review LLM completions, and incorporate feedback, which then powers automatic dataset curation. Log10 also features a declarative test suite for continuous evaluation, integrating with CI/CD pipelines to prevent hallucinations as models and prompts evolve.
Target Audience
Log10 is designed for AI development teams in high-stakes, regulated industries such as healthcare, finance, insurance, and law, who require an end-to-end AI accuracy solution.
Features
- Real-time monitoring and alerts for critical errors, providing insights into application performance.
- AutoFeedback feature that instantly evaluates LLM completions with expert-level precision using limited samples.
- Streamlined Inbox for expert review of LLM completions, incorporating feedback from end-users through the API.
- Declarative test suite for continuous evaluation, seamlessly utilizing platform-curated datasets.
- LLM IDE for debugging and addressing prioritized errors, leveraging a real-time AutoFeedback accuracy signal.
- Tools for fine-tuning prompts and models, enhancing application accuracy as production feedback increases.
- Secure architecture with data ownership, privacy, and built-in safeguards to minimize risks like bias or misuse.