Pienso provides a no-code platform for training and deploying customized Large Language Models (LLMs) using both structured and unstructured data, enabling users to categorize, label, and analyze their data efficiently. The solution ensures data privacy by operating in the user's environment, allowing businesses to gain real-time insights while maintaining control over their sensitive information.
Funding
$29.2M 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.
LVFounders
Product
Problem
Many organizations struggle to efficiently analyze large volumes of unstructured text data, such as customer feedback, documents, and communication logs, to extract meaningful insights. Existing solutions often require extensive coding or specialized expertise, making it difficult for subject matter experts to directly leverage their knowledge in the analysis process. Furthermore, concerns about data privacy and control can hinder the adoption of cloud-based LLM solutions.
Solution
Pienso offers a no-code platform that enables users to train and deploy custom Large Language Models (LLMs) for analyzing both structured and unstructured data. The platform's interactive interface allows subject matter experts to categorize, label, and analyze data without writing any code, effectively imprinting their expertise at scale. By operating within the user's environment, Pienso ensures data privacy and control, allowing businesses to gain real-time insights while adhering to their enterprise security policies. The platform supports the entire lifecycle of LLM development, from data ingestion and model training to annotation and deployment, providing a transparent and customizable solution for extracting valuable information from text data.
Target Audience
Pienso targets subject matter experts, data scientists, and analysts across various industries who need to analyze large volumes of text data to gain customer insights, improve content moderation, enhance document intelligence, and mitigate cyber risks.
Features
- No-code interface for training and deploying custom LLMs, accessible to non-technical users
- Fingerprinting Workspace for refining initial categorizations and training models with user knowledge
- Annotation tool for quickly labeling datasets using trained models
- Data Set Analysis for fine-tuning models and discovering insights through visualizations
- PromptFactory for experimenting, evaluating, and deploying production-caliber prompts
- Option to deploy fine-tuned LLMs in the user's own environment (cloud or on-premises)
- Full control over models, ensuring reliability and preventing unexpected parameter changes
- Support for both structured and unstructured data ingestion