About Big Hummingbird

Big Hummingbird offers a drag-and-drop platform for building production-grade LLM automations, featuring workflow-based prompt management and one-click deployment as REST endpoints. The platform enables users to refine prompts, gather human feedback, and integrate external data, addressing the challenges of inconsistent LLM responses and the complexities of AI application deployment.

```xml <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. </problem> <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. </solution> <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. </features> <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. </target_audience> ```

What does Big Hummingbird do?

Where is Big Hummingbird located?

Big Hummingbird is based in Austin, United States.

When was Big Hummingbird founded?

Big Hummingbird was founded in 2024.

Who founded Big Hummingbird?

Big Hummingbird was founded by Jennifer Jheng.

Jennifer Jheng - Co-founder

Location

Austin, United States

Founded

2024

Employees

2 employees

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Big Hummingbird

Score: 29/100

AI-Generated Company Overview (experimental) – could contain errors

Executive Summary

bighummingbird.com 10+

Founded 2024 – Austin, United States

Funding

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Team (<5)

Jennifer Jheng

Co-founder

Company Description

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.

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.

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.