Bricks

Converts natural language queries and instructions into spreadsheet formulas (SQL-like or Excel syntax) that execute within the spreadsheet grid. The system parses user intent, maps it to available cell data and functions, generates appropriate formula syntax, and evaluates results in-cell. This enables non-technical users to perform calculations without manual formula writing.

Applies machine learning-based transformations to raw data within the spreadsheet, including deduplication, standardization, type inference, and pattern-based cleaning. The system analyzes column data, detects common issues (inconsistent formatting, missing values, duplicates), and applies transformations either automatically or with user confirmation. Works by sampling data, inferring intent, and applying vectorized operations across rows.

Predicts and suggests cell values, formulas, or data entries based on column context, previous entries, and patterns in the spreadsheet. Uses sequence modeling (likely transformer-based) to analyze column history and adjacent data, then surfaces ranked suggestions as the user types or selects a cell. Integrates with the spreadsheet UI to show suggestions inline without interrupting workflow.

Connects to external data sources (databases, APIs, CSV files, cloud storage) and allows querying/importing data directly into the spreadsheet using natural language or structured queries. The system manages connection credentials, translates user intent into source-specific queries (SQL, REST API calls, etc.), and materializes results as spreadsheet rows/columns. Handles schema mapping and type coercion automatically.

Enables multiple users to edit a spreadsheet simultaneously with AI-powered suggestions, conflict resolution, and contextual comments. The system tracks changes, detects conflicts when multiple users edit the same cell, uses AI to suggest merge strategies, and allows users to leave AI-enhanced comments (e.g., 'explain this formula' or 'flag data quality issues'). Built on operational transformation or CRDT-based sync to handle concurrent edits.

Generates formatted reports, dashboards, and visualizations from spreadsheet data using natural language descriptions or templates. The system analyzes the data structure, infers appropriate chart types (bar, line, pie, etc.), applies styling and branding, and exports reports in multiple formats (PDF, HTML, PowerPoint). Uses layout algorithms to arrange visualizations and text for readability.

Applies machine learning models (time series forecasting, regression, classification) to spreadsheet data to predict future values, identify trends, or classify records. The system automatically selects appropriate model architectures based on data characteristics, trains on historical data, and generates predictions with confidence intervals. Results are materialized as new columns or charts in the spreadsheet.

Enables users to define automated workflows triggered by spreadsheet events (cell changes, data imports, scheduled times) that execute actions like sending notifications, updating other cells, or calling external APIs. The system provides a visual workflow builder or natural language interface to define conditions (IF cell > 100, THEN send email) and actions, then executes them asynchronously. Uses event-driven architecture with a rules engine.

Automatically detects data quality issues (type mismatches, out-of-range values, missing required fields, constraint violations) by inferring or accepting explicit schemas for spreadsheet columns. The system validates incoming data against the schema, flags violations with severity levels, and suggests corrections. Works by analyzing column metadata, data samples, and user-defined rules to build a validation model.