The world's first AI-native data science canvas for prompt-to-insight workflows.
Einblick, acquired by Postman in 2024, has evolved into a cornerstone of AI-driven data exploration and API intelligence heading into 2026. Unlike traditional linear notebooks like Jupyter or static BI dashboards like Tableau, Einblick utilizes a multi-modal visual canvas. Its core engine is powered by 'Enzo,' a proprietary LLM agent specifically fine-tuned for data science tasks including data cleaning, SQL generation, and Python-based predictive modeling. The technical architecture focuses on an 'agentic' workflow where users provide high-level natural language objectives, and the system autonomously constructs the necessary data pipelines and visualizations. In the 2026 landscape, Einblick differentiates itself by deeply integrating with the Postman API ecosystem, allowing developers and data scientists to move seamlessly from API data extraction to advanced machine learning modeling. Its 'incremental computation' engine ensures that large datasets can be explored interactively without the latency typically associated with cloud data warehouses. It bridges the gap between low-code accessibility and high-code flexibility, making it a primary tool for rapid prototyping in agile enterprise environments.
A specialized agentic LLM that converts complex natural language prompts into executable Python and SQL blocks.
Turn natural language into production-grade SQL and instant visual insights with RAG-enhanced schema awareness.
Autonomous data synthesis and predictive modeling for the modern AI-driven enterprise.
Turn your databases and spreadsheets into intelligent conversational AI agents.
The conversational AI analyst that transforms your database into an interactive dialogue.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Processes data in smaller chunks to provide immediate visual feedback even on multi-gigabyte datasets.
Automated feature engineering and model selection within the visual canvas workspace.
Automatically maps the flow of data from source to visualization on the canvas.
Real-time multi-user editing environment similar to Figma but for data science.
Direct ingestion of API response data into the analytical canvas for real-time monitoring.
Users can write custom Python functions (User Defined Functions) directly into the canvas blocks.
Reducing the time from data raw export to building a predictive churn model from weeks to hours.
Registry Updated:2/7/2026
Correlating API latency data from Postman with business sales data.
Enabling finance teams to run 'What-If' scenarios visually.