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What is ReByte?

ReByte's goal is to democratizing coding work within your team, especially for tasks that previously require hundreds to a few thousands of lines of code. we believe that everyone in your team should be able to build user-centric apps to automate their daily tasks.

Code agent here means agent that write their actions in code (as opposed to "agents being used to write code").

Unlike other code agents, ReByte focuses on helping non programmer build user-centric apps from your internal data sources. These apps can identify and act on meaningful insights from the data, while ensuring the factual integrity of their results. They can even help you ask the questions you haven’t thought of yet and find the answers. This leads to deeper insights and more impactful decision-making across the business.

Specifically, our focus is on:

  • OLAP tasks that require complex SQL queries and data visualization
  • Tasks that typically require hundreds to a few thousands of lines of code, could be python, javascript, SQL, bash, etc
  • Any combination of the above two categories

Data source could be public data, or internal data owned by your company, such as csv, excel, postgres, mysql, parquet, iceberg, snowflake, databricks, s3, bigquery etc.

ReByte accomplishes this goal by providing three main components:

1. A code agent to build 'Burn after Use' apps: With LLM's ability to write code, apps become truly "burn after use." We call it user-centric apps. With the help of agent compute unit, Rebyte can act as a code agent for internal applications, iteratively writing and running code to accomplish the user's tasks.

2. A translation layer from data source to LLM friendly data unit: An abstraction of a two dimension table that can be pulled from various sources, such as csv, Excel, postgres, mysql, parquet, snowflake, s3, bigquery etc. Those Data units are enriched with metadata, such as column type, column name, column description, possible values, max/min values, whether it's a primary key, whether it's a NULL value etc, with those metadata, LLM can better understand the data, thus achieve super high accuracy from natural language to SQL.

3. Agent builder to capture business-specific workflows: ReByte workflow is a sequence of actions driven by an LLM to achieve a specific goal. These actions can include LLM calls, loading database schemas, querying databases, performing internet searches, running JavaScript code, calling external services, and more. The ReByte Builder is a web-based tool that allows you to create these agents and deploy them to production with just a few clicks. The primary goal of ReByte Agent is to capture and automate your team's workflow.

For the above components, 2 and 3 are for developers in your team, 1 is for every member of your team.

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Besides, ReByte offers the following features:

State-of-the-Art Analytics Performance: ReByte can ingest millions of rows of data in just a few seconds. It handles complex queries, including joins, group by operations, window functions, and more, with ease.

Model Agnostic: In the Agent Builder, you can use any large language model, such as OpenAI, Gemini, Anthropic, Deepseek, or any other open-source models provided by ReByte. You’re not limited to a specific model.

Team Collaboration: Building high-quality AI agents is a team effort. ReByte’s Agent Builder is a web-based tool that enables team members to collaborate in real-time, just like editing a document in Google Docs.

Observability: ReByte's goal is to make AI agents transparent, not black-box systems. We’ve built a monitoring system that allows you to track how your agents are performing and how your data is being used.

Secure Sandbox: Each agent operates in its own secure sandbox, so you never have to worry about data leaks between agents.

Access Control: ReByte includes a role-based access control system, allowing you to manage who can access specific data, who can build agents, and who can use them.

API: Agents built in ReByte can be accessed via an API, making it easy to integrate ReByte with your existing systems.

Typical Use Cases

We expect ReByte can be useful in the following use cases:

  • New Kinds of BI Tools LLMs have revolutionized data visualization. With ReByte, you can generate visualizations automatically, without being limited to the types provided by traditional BI tools. You can create any kind of visualization you need, tailored to your data.

  • Internal Data Exploration and Analysis Data owners can create a virtual database with proper access control and share it with other team members. Everyone can then run queries independently on the shared database. For example, if you have sales data across Google, Excel, local CSV files, and databases, you can use ReByte Table to combine and explore your data in flexible ways, helping you identify the best sales channel for your company.

  • General Repetitive Tasks Repetitive tasks such as data cleaning, data transformation, file processing, and statistical analysis, which were previously handled by programmers, can now be done by anyone in your team with ease.

Typically, tasks that would require hundreds or thousands of lines of code can be fully automated by an agent. We're actively working to extend ReByte's capabilities to cover more use cases.