Conversational Data Engineering With Express
Express is a conversational data engineering platform powered by AI agents.
Describe what you need in natural language, and Express helps discover, connect, transform, and prepare the data required for the task.
Key takeaways:
- Natural Language Data Engineering: Describe what you need and let Express build the data workflow without coding.
- Agentic Data Exploration: Express explores existing sources, schemas, and transformations to find and reuse the right data.
- Generative UI: Express dynamically creates interactive views and inputs based on the task you’re trying to complete.
- Agentic Context Engineering: Express uses existing workflows and context to help manage complex, multi step data engineering tasks.
- MCP Servers For AI Agents: With MCP Studio, teams can create governed, task specific MCP servers that give AI agents access to the data, tools, and actions they need.
- Built for Enterprise: Express is built on Nexla’s enterprise data platform with security, governance, monitoring, and connectivity across enterprise systems.
- Works Across Technical And Business Teams: Developers, analysts, and business users can use natural language to work with enterprise data without requiring every task to start with custom code.
What Is Express?
Express brings conversational AI to data engineering, letting developers, analysts, and business users describe data tasks in natural language. Express helps discover, connect, transform, and prepare enterprise data, while reusing existing sources, schemas, and workflows.
Express also extends data engineering into agent-ready workflows. With MCP Studio, teams can create governed, task-specific MCP servers that connect AI agents to enterprise data, tools, and actions.
Because Express runs on the Nexla platform, it provides enterprise capabilities for security, monitoring, governance, and data integration.
How Express Works
1. Describe What You Need
Start with the outcome you want in natural language. You do not need to begin by defining a pipeline or writing integration code.
2. Explore Available Data
Express uses agentic exploration to identify relevant sources, schemas, and existing transformations that can contribute to the task.
3. Connect And Transform
Express determines how the required data should be connected and transformed for the intended use.
4. Build The Workflow
The conversational experience guides the creation of the data workflow, using existing context and knowledge to help manage complex, multi-step tasks.
5. Prepare Data For Use
The resulting workflow can prepare data for analytics, applications, and AI workloads. With MCP Studio, teams can also create governed, task-specific MCP servers that give AI agents access to the data, tools, and actions they need.
What Makes Express Different?
- Generative UI: Express dynamically creates interactive views and inputs based on the task, giving users an interface designed around what they are trying to accomplish.
- Agentic Exploration: Express explores existing enterprise sources, schemas, and transformations to identify relevant data and reuse what is already available.
- Agentic Context Engineering: Express uses context from existing data workflows to help handle complex, multi-step tasks and determine appropriate next steps.
- Enterprise Data Foundation: Express is built on Nexla’s enterprise data platform, connecting to enterprise systems while providing governance, monitoring, and security.
Built For Real World Data Engineering
Express brings AI-powered automation to the data engineering work between enterprise systems and the applications, analytics, and AI workloads that depend on them.
Developers can use natural language to build and work with data workflows. Analysts can access and prepare data without relying on custom engineering for every task. Business users can describe the outcome they need and work from there.
Express also helps teams move from data workflows to agent-ready workflows. With MCP Studio, teams can create governed, task-specific MCP servers that connect AI agents to enterprise data, tools, and actions.
The result is a more conversational way to work with enterprise data while maintaining the connectivity, governance, and controls required for production environments.
Next steps: