Nexla recently introduced Express, a conversational data engineering platform designed to dramatically lower the barrier to building data pipelines for AI applications. The platform allows users to describe the data they need in natural language (for example, “Take customer data from Salesforce and combine it with Google Analytics to create a data product”). The system automatically discovers, connects, and transforms appropriate sources into secure, production-ready pipelines.
Express is powered by Nexla’s agent AI framework, which understands user intent, identifies data, and handles transformations without the need for manual coding. This enables developers, analysts, and even business users to self-service their data needs in minutes, eliminating the delays and complexities often associated with traditional data engineering efforts.
The platform is available immediately as a standalone product with usage-based pricing, making it available to individuals and small teams. Nexla says Express complements its extensive data integration platform and will help accelerate new user onboarding and adoption.
Nexla also outlines various use cases for Express. Analysts can build customer dashboards in minutes. AI engineers can piece together rich context from databases, documents, and APIs. Claims teams can transform insurance contract documents into structured data. Marketing managers can connect campaign and sales systems to measure ROI in real time. Operations teams can aggregate logistics data across multiple providers without writing pipeline code.
In a GlobeNewswire article, Nexla CEO and co-founder Saket Saurabh described the launch as “data engineering transformed into context engineering,” adding that the platform allows “anyone who knows the desired outcome…to interact with data conversationally and reliably in minutes.”
Under the hood, Express builds on Nexla’s existing metadata-driven integration technology and extends it with conversational agent capabilities. Earlier this year, Nexla expanded its agent AI foundation through tight integration with no-code GenAI tools and search augmentation generation (RAG) pipeline architecture.
By comparison, Databricks offers Databricks Assistant, which also supports conversational queries against your data. This assistant helps users generate SQL or Python code, explain complex queries, and even fix code errors, all within the context of the user’s workspace. It’s suitable for data engineers and analysts familiar with Databricks, but it doesn’t fully automate end-to-end pipeline assembly in the same way as Express. It’s more of a code generation and support tool than a no-code workflow builder.
Snowflake is also a good candidate. Using Snowflake Intelligence, the company is building conversational agents on top of its data platform. Cortex Agent enables inference on structured and unstructured data, Cortex Analyst supports SQL queries from natural language, and Cortex Search enables semantic search against document repositories. This architecture provides rich context with metadata and lineage for conversational AI, but unlike Express, Snowflake’s model is tightly coupled to its own storage and compute engine rather than offering a broader “pipeline-as-agent” experience. /p>
In the open source space, Airbyte is often used for data integration, but currently does not focus on conversational or agent-based data workflows. Focused on traditional ETL/ELT pipelines. Platforms like KNIME, on the other hand, offer visual, modular data flow construction with no-code interfaces, but lack popular natural language conversations and autonomous agent orchestration.
With Express, Nexla aims to solve critical bottlenecks in AI deployment and make it faster, easier, and more secure for all users, not just data engineers, to prepare data for AI. The company is positioning the new tool as a key tool to enable “AI-enabled” data access at the speed and scale that modern enterprises demand.
