Big Hummingbird provides cloud-based data pipeline orchestration and workflow management for complex ETL processes. The platform enables engineering teams to reliably schedule, monitor, and scale data transformations across diverse cloud data warehouses and storage systems. This service ensures data integrity and timely delivery necessary for business intelligence and machine learning applications.
Funding
Funding not disclosed
Founders
Product
Problem
Building production-ready applications with Large Language Models (LLMs) is challenging due to the inconsistent nature of LLM responses and the complexities involved in prompt engineering, testing, and deployment. Traditional development practices often fall short when applied to LLMs, requiring specialized tools for prompt management, evaluation, and integration with external data sources.
Solution
Big Hummingbird offers a drag-and-drop platform designed to streamline the development and deployment of LLM-powered automations. The platform provides a workflow-based prompt management system with integrated playground testing and criteria-based evaluations, enabling users to refine and improve prompts with confidence. It supports multiple LLM providers, including OpenAI, Anthropic, and Google, and facilitates Retrieval Augmented Generation (RAG) by allowing users to integrate their own data. With one-click deployment as REST endpoints, Big Hummingbird simplifies the process of bringing AI automations into existing systems, complete with feature flags and A/B testing capabilities.
Target Audience
Big Hummingbird targets developers, data scientists, and product teams looking to build and deploy production-grade LLM automations without the complexities of traditional coding and infrastructure management.
Features
- Drag-and-drop interface for building LLM workflows without extensive coding.
- Integrated prompt playground for real-time testing and refinement.
- Workflow-based prompt management with version control and rollback capabilities.
- Support for multiple LLM providers, including OpenAI GPT, Anthropic Claude, and Google Gemini.
- Retrieval Augmented Generation (RAG) for incorporating external data sources.
- Built-in evaluation system for gathering human feedback and generating synthetic test data.
- Bias detection and removal tools using Z-score normalization.
- One-click deployment as REST endpoints for easy integration.
- Feature flags and A/B testing capabilities for controlled rollouts.
- Support for Pinecone vector database for fast and scalable vector searches.