Rasgo

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Rasgo revolutionizes self-service analytics with AI-powered insights, secure data handling, and natural language queries.

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What is Rasgo?

Rasgo is a generative AI tool developed to revolutionize self-service analytics by empowering users to make data-driven decisions efficiently. It utilizes natural language queries, data visualization, and automated intelligent insights, enabled by GPT-4 technology. Rasgo ensures data security by processing Enterprise Data Warehouse (EDW) data securely without moving or copying any source data, providing tailored insights, and offering a natural language interface for effective AI analytics interaction.

Who created Rasgo?

Rasgo was founded in 2020 by a team of data community professionals who aimed to enhance data accessibility and trust within organizations. The company's mission is to empower users with valuable and trusted data insights efficiently. It was launched on May 25, 2023, with a focus on providing generative AI tools for self-service analytics. Rasgo has raised over $25 million in funding, emphasizing a commitment to transforming enterprise analytics and enhancing transparency in AI interactions .

What is Rasgo used for?

Revolutionizing self-service analytics
Providing users with the ability to make data-driven decisions more efficiently
Natural language query
Data visualization
Automated intelligent insights
Empowering users with valuable and trusted insights from data faster
Utilizing GPT-4 for autonomous agents to deliver insights directly from Enterprise Data Warehouse (EDW)
Prioritizing data security by securely utilizing GPT to process EDW data without moving or copying source data
Enabling proactive decision-making by continuously seeking valuable insights
Creating lasting intellectual value within an organization through generative insights

Who is Rasgo for?

Data Analysts
Business analysts
Data scientists
Executives

How to use Rasgo?

To use Rasgo, follow these steps:

Access the Platform: Log in to the Rasgo platform to start utilizing its features.
Ask Questions: Use natural language to inquire about data insights directly from your Enterprise Data Warehouse (EDW).
Visualize Data: View data visually within the platform for easier comprehension and analysis.
Receive Intelligent Insights: Benefit from automated intelligent insights based on your data goals, helping you make informed decisions.
Utilize Autonomous Agents: Rasgo creates autonomous GPT-4-enabled agents that deliver tailored insights from your data without manual analysis.
Ensure Data Security: Your raw data remains in your EDW, with Rasgo processing it securely using GPT without moving or copying any source data.
Track Interactions: Every AI-enabled interaction is logged in your EDW and transparently discoverable within the application.
Generate Lasting Value: Rasgo aims to provide generative insights that create enduring intellectual value within your organization.
Enable Proactive Decision-Making: Benefit from Rasgo's self-learning capabilities to guide itself through your data environment, optimizing for valuable insights.
Look Towards the Future: Rasgo envisions generating strategic analytical products from a variety of data sources for enhanced decision-making.

By following these steps, you can leverage Rasgo's advanced capabilities in self-service analytics and data-driven decision-making.

Pros

Rasgo aims to revolutionize self-service analytics by providing users with the ability to make data-driven decisions more efficiently
It offers features such as natural language query, data visualization, and automated intelligent insights
Rasgo creates autonomous agents that deliver insights directly from the Enterprise Data Warehouse (EDW), eliminating the need for users to manually analyze data
It reads and understands data, providing tailored insights and rich business context, including visualizations
Rasgo prioritizes data security by securely utilizing GPT to process EDW data without moving or copying any source data
It aims to provide generative insights that create lasting intellectual value within an organization
Rasgo enables proactive decision-making by utilizing its specialized knowledge of the enterprise and continually seeking valuable insights
It envisions a connected future where it can generate strategic analytical products based on a wide range of data sources
Rasgo highlights GPT-4's capabilities in complex reasoning tasks, self-learning, and self-teaching, allowing for a collaborative and perpetually learning AI system
It strives to transform enterprise analytics and offers a demo to showcase its potential in self-service analytics powered by GPT

Cons

Even with the centralization of data, organizations still struggle to capture complete trust and democratization over insights from data, leading to poor decisions made on inconsistent data
Data team spends significant time centralizing data in the cloud while the business reports on inconsistent metrics calculated in siloed spreadsheets and BI tools
There might be issues with trust and accessibility of insights from data when using Rasgo
Rasgo may lack certain features or capabilities compared to other AI tools in the industry
It is important to consider if Rasgo justifies its value for the price charged
The need for change in making data-driven decisions faster with complete trust and accessibility
Rasgo strives to transform enterprise analytics, which may bring about challenges and adjustments in the organization
Organizations may experience frustration from both data and business teams due to poor decisions made on inconsistent data
The tool's goal of fostering greater speed and trust in data insights may face obstacles or limitations in practice
There may be challenges related to the centralization of data and accessing valuable and trusted insights effectively

Pros

Cons

Rasgo aims to revolutionize self-service analytics by providing users with the ability to make data-driven decisions more efficiently
It offers features such as natural language query, data visualization, and automated intelligent insights
Rasgo creates autonomous agents that deliver insights directly from the Enterprise Data Warehouse (EDW), eliminating the need for users to manually analyze data
It reads and understands data, providing tailored insights and rich business context, including visualizations
Rasgo prioritizes data security by securely utilizing GPT to process EDW data without moving or copying any source data
It aims to provide generative insights that create lasting intellectual value within an organization
Rasgo enables proactive decision-making by utilizing its specialized knowledge of the enterprise and continually seeking valuable insights
It envisions a connected future where it can generate strategic analytical products based on a wide range of data sources
Rasgo highlights GPT-4's capabilities in complex reasoning tasks, self-learning, and self-teaching, allowing for a collaborative and perpetually learning AI system
It strives to transform enterprise analytics and offers a demo to showcase its potential in self-service analytics powered by GPT

Even with the centralization of data, organizations still struggle to capture complete trust and democratization over insights from data, leading to poor decisions made on inconsistent data
Data team spends significant time centralizing data in the cloud while the business reports on inconsistent metrics calculated in siloed spreadsheets and BI tools
There might be issues with trust and accessibility of insights from data when using Rasgo
Rasgo may lack certain features or capabilities compared to other AI tools in the industry
It is important to consider if Rasgo justifies its value for the price charged
The need for change in making data-driven decisions faster with complete trust and accessibility
Rasgo strives to transform enterprise analytics, which may bring about challenges and adjustments in the organization
Organizations may experience frustration from both data and business teams due to poor decisions made on inconsistent data
The tool's goal of fostering greater speed and trust in data insights may face obstacles or limitations in practice
There may be challenges related to the centralization of data and accessing valuable and trusted insights effectively

Rasgo FAQs

What is Rasgo?: Rasgo is a generative AI tool that aims to revolutionize self-service analytics by providing users with the ability to make data-driven decisions more efficiently.

How does Rasgo utilize GPT-4?: Rasgo utilizes GPT-4 by creating autonomous agents that deliver insights directly from the Enterprise Data Warehouse (EDW), reading and understanding data to provide tailored insights and rich business context.

How does Rasgo ensure data security?: Every AI-enabled interaction is logged in the EDW and can be tracked within the application, ensuring transparency and trust. Raw data never leaves the EDW, and GPT processes data without moving or copying any source data.

What are generative insights according to Rasgo?: Rasgo believes that generative insights should create lasting intellectual value within an organization, empowering teams to make smarter, data-informed decisions while reducing the time spent on creating knowledge products by 80%.

What is Rasgo's vision for the future?: Rasgo envisions a connected future where it can generate strategic analytical products based on a wide range of data sources, utilizing GPT-4's capabilities in complex reasoning tasks and perpetual learning.

What is the mission of Rasgo?: The mission of Rasgo is to empower valuable and trusted insights from data in less than 5 minutes, driven by a commitment to trust and data accessibility.

How does Rasgo differentiate itself in the analytics market?: Rasgo differentiates itself by offering natural language interfaces, autonomous agents for data analysis, and prioritizing trust, security, and lasting intellectual value in insights.

How does Rasgo support collaboration between humans and AI?: Rasgo enables human-AI collaboration via GPT-powered AI agents that excel in complex reasoning tasks, self-learning, and self-teaching, creating a perpetual learning machine.