Conversational data intelligence for seamless spreadsheet exploration and automated insights.
DeepSheet Analytics represents the 2026 standard for agile data exploration, moving beyond static pivot tables into dynamic conversational intelligence. At its core, the platform utilizes a specialized Large Language Model (LLM) orchestration layer optimized for structured data reasoning (Text-to-Python and Text-to-SQL). Unlike traditional BI tools that require steep learning curves, DeepSheet allows users to interact with CSV, Excel, and JSON files through a natural language interface, effectively democratizing data science across the enterprise. Its technical architecture prioritizes data privacy through local browser-side processing capabilities for sensitive datasets, while its cloud compute handles massive multi-gigabyte files. Positioned as a 'Shadow BI' disruptor, it integrates seamlessly into existing workflows, providing instant visualization generation, anomaly detection, and predictive forecasting without manual formula writing. For 2026, it has introduced autonomous agent features that can monitor data updates via webhooks and proactively alert stakeholders to significant trend shifts or data integrity issues, making it an indispensable asset for fast-moving sales, marketing, and finance teams.
Uses embeddings to understand columns contextually, even if headers are misspelled or in foreign languages.
The open-source BI platform that turns your dbt project into a governed, version-controlled analytics engine.
Transform raw data into real-time metrics with a powerful semantic layer and automated BI dashboards.
The AI-powered data scientist that automates complex analysis, visualization, and predictive modeling through sandboxed code execution.
The world's most adaptable EPM platform for autonomous financial and operational planning.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Background agents monitor connected datasets for statistical anomalies or target threshold breaches.
LLM-guided VLOOKUP and JOIN logic that identifies primary keys automatically across disparate files.
Option to process and mask PII (Personally Identifiable Information) before data hits the cloud inference engine.
Converts any processed dataset and query result into a permanent REST API endpoint.
Integrates Prophet and ARIMA models to provide 30-60-90 day forecasts from historical time-series data.
Allows users to view and export the underlying Python/Pandas code used to generate an insight.
Identifying low-performing regions across thousands of rows of transaction data.
Registry Updated:2/7/2026
Predicting when stock will run out based on velocity and lead times.
Linking usage patterns to subscription cancellations.