Jestor

Provides a drag-and-drop interface for constructing multi-step automation sequences with conditional logic, loops, and error handling without writing code. The builder uses a node-based graph architecture where each node represents an action (API call, data transformation, notification) and edges define execution flow. Conditions are evaluated at runtime to branch execution paths, and the platform compiles visual workflows into executable state machines that run on Jestor's backend infrastructure.

Accepts plain-English descriptions of business processes and uses LLM inference to generate draft automation workflows with pre-configured nodes, conditions, and data mappings. The system parses the user's intent, maps it to available actions and data sources in the workspace, and generates a visual workflow template that users can review and refine. This reduces configuration time by pre-populating common patterns (approval chains, data syncs, notifications) based on semantic understanding of the process description.

Enables processing large datasets (thousands to millions of records) through bulk operations like mass updates, deletions, or transformations without manual iteration. Users define a filter to select records and an action to apply (update field values, run a workflow for each record, export to file). The platform queues bulk jobs and processes them asynchronously with progress tracking, allowing users to monitor completion status and view results. Bulk operations are optimized for performance, processing records in batches to avoid timeout issues.

Enables creating visual dashboards that display real-time summaries of database data through charts, tables, and KPI cards. Users select data sources, define aggregations (sum, count, average, group by), and choose visualization types (bar charts, line graphs, pie charts, tables). Dashboards update automatically as underlying data changes, and users can filter dashboard views by date range, category, or other dimensions. Reports can be scheduled for email delivery or exported to PDF format.

Provides pre-built templates for common internal tools (CRM, inventory management, project tracking, expense tracking) and automation workflows (approval chains, data syncs, notifications). Templates include pre-configured database schemas, app layouts, and workflow definitions that users can customize for their specific needs. Templates accelerate time-to-value by providing a starting point rather than building from scratch, and include best-practice patterns for common business processes.

Provides a visual interface for creating internal business applications by combining pre-built UI components (forms, tables, dashboards, charts) with a backend database schema. Users define data models, create forms for data entry, and automatically generate CRUD interfaces without writing HTML/CSS/JavaScript. The platform uses a component-based architecture where each UI element binds directly to database fields, and business logic is added through workflows or simple field-level rules rather than custom code.

Enables connecting to external data sources (APIs, databases, CSV uploads, SaaS platforms) and transforming data through visual mapping interfaces without SQL or scripting. The platform provides a schema inference engine that automatically detects field types and relationships from source data, then allows users to map source fields to destination database fields with optional transformations (concatenation, date formatting, value mapping). Data can be synced on a schedule or triggered by events, with built-in deduplication and conflict resolution strategies.

Executes workflows on a schedule (hourly, daily, weekly, monthly) or in response to events (database record creation, form submission, webhook trigger, external API event). The platform uses a job scheduler backend that manages workflow invocation timing and maintains execution history with logs. Event-based triggers use webhook listeners or database change detection to initiate workflows in near real-time, while scheduled workflows run on specified intervals with configurable timezone support and execution retry logic.

Implements user authentication and authorization through role definitions that control access to apps, workflows, and data records. Users are assigned roles (e.g., Admin, Manager, Viewer) which determine which apps they can access and which database records they can view/edit. The platform supports field-level permissions (hiding sensitive columns) and record-level filtering (showing only records where a user field matches the logged-in user). Permissions are enforced at the API and UI level, preventing unauthorized data access even if users attempt direct API calls.

Enables multiple users to edit database records simultaneously with automatic conflict resolution and change tracking. When two users edit the same field, the platform uses last-write-wins or merge strategies to resolve conflicts, and both users see real-time updates via WebSocket connections. Change history is maintained for audit purposes, showing who modified which fields and when. The system prevents data corruption through optimistic locking and transaction isolation at the database level.

Automatically generates REST API endpoints for database tables, allowing external systems to read and write data without manual API development. Each table gets standard CRUD endpoints (GET, POST, PUT, DELETE) with automatic OpenAPI documentation. Users can configure which fields are exposed, add authentication requirements, and set rate limits per endpoint. The generated APIs support filtering, sorting, and pagination through query parameters, and return responses in JSON format with proper HTTP status codes.

Allows Jestor workflows to receive and respond to webhook events from external systems (e.g., Stripe payment events, GitHub push notifications, Slack messages). Users configure webhook endpoints that trigger specific workflows when events are received, with automatic payload parsing and field mapping. The platform validates webhook signatures to ensure authenticity and provides retry logic for failed webhook deliveries. Incoming webhook data is automatically mapped to workflow variables, enabling downstream actions to process external events.

Uses machine learning models to automatically detect invalid or anomalous data entries in database records. The system learns patterns from historical data and flags entries that deviate significantly from expected values (e.g., unusually high invoice amounts, invalid email formats, suspicious user behavior). Validation rules can be configured per field with custom thresholds, and anomalies trigger notifications or workflow actions. The platform provides explanations for why data was flagged, helping users understand the reasoning behind automated decisions.