Product Forge is a tool designed to streamline the creation of high-quality Epics, Features, Stories, and Bugs directly within platforms like Jira, Azure DevOps, and Linear. It leverages Generative AI and user-provided product information to automate and enhance the process of capturing business needs, saving time, increasing capacity, and ensuring the creation of detailed and well-structured user stories.
Product Forge
AI-powered tool for effortless user story creation in Jira, Azure DevOps, etc.
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What is Product Forge?
How to use
Product Forge integrates directly into platforms like Jira and Azure DevOps. Users can generate stories in bulk, transform requirements documents into user stories with a single click, and improve existing stories by adding context, tech stack details, and persona information. The AI can be directed with customizable templates to control the output.
Core Features
- Bulk Story Creation
- Requirements to Stories Conversion
- Existing Story Improvement
- Customizable Templates
Use Cases
- Quickly generate multiple user stories for a new feature.
- Transform a detailed requirements document into a set of actionable user stories.
- Enhance an incomplete user story with missing context and details.
FAQ
What platforms does Product Forge integrate with?
Product Forge integrates seamlessly with Google Docs, Linear, Jira, Azure DevOps (Azure Boards), Asana, and ClickUp.
Is my data used for training the AI models?
No, none of your data is used for training our models. We ensure your information remains secure and confidential.
What information does Product Forge use to generate user stories?
Product Forge uses information available from the page you're on (e.g., the 'Create Issue' page in Jira or Azure Dev Ops), including the title and description of the story. It also uses text from your clipboard when generating stories from documents.
Pricing
Pros & Cons
Pros
- Significant time savings in story creation.
- Consistent quality and well-formatted stories.
- Customizable output to fit specific needs.
- Seamless integration with popular platforms.
- Secure data handling with no data used for model training.
Cons
- Reliance on the quality of input data for optimal AI performance.
- Potential learning curve for customizing templates effectively.