Monitor webhook data, check each record against configurable rules, log results, and notify stakeholders with a structured validity report.
The AI agent validates every imported record against a configurable set of rules. It checks required fields, data types, formats, and constraints across all rows. It returns a structured report with totals and detailed errors to resolve data issues before loading into ERP, CRM, or databases.
Executes end-to-end validation on every import, surfacing both problems and the exact fixes.
Ingests the webhook payload containing data and rules and normalizes field formats.
Validates each field in every row against the configured rules (required, type, dateFormat, regex, etc.).
Logs results and aggregates a summary with total, valid, and invalid counts.
Generates a detailed per-row error list with row number, field name, failed rule, and error message.
Applies default rules when none are provided and allows per-request overrides for multiple schemas.
Presents the report in a structured format suitable for downstream systems like ERP/CRM.
Before: data imports often fail due to missing fields, type mismatches, invalid formats, and inconsistent schemas. After: imports are validated in real-time against configurable rules, producing clean data and clear remediation guidance.
A simple, three-step flow that non-technical users can follow.
Receives data and rules from the webhook, normalizes formats, and readies them for validation.
Applies required, type, min/max, length, regex, enum, and dateFormat checks to every field in every row.
Compiles a totals summary and per-field errors, then returns the report via API or webhook.
A realistic data-import scenario and expected outcome.
A nightly import pushes 1,200 customer records as JSON to the webhook. The agent validates against rules (required name and email, dateFormat YYYY-MM-DD, and proper email format). Within 2 minutes, the report shows 950 valid rows and 250 invalid rows with precise errors for each failed record. The data team uses the report to correct the data and re-upload, ensuring only clean records enter the ERP system.
Roles that rely on clean data in downstream systems.
Prevent bad data from entering critical systems by validating at import time.
Identify and fix data quality issues quickly during imports.
Ensure only compliant records are loaded into ERP/CRM.
Easily generate audit trails with per-row validation details.
Embed consistent validation rules into pipelines.
Maintain data governance by enforcing format and privacy constraints.
Built-in connectors and nodes that enable seamless validation.
Receives data and rules from the webhook and triggers validation.
Applies required, type, min/max, length, regex, enum, and dateFormat checks.
Standardizes field types and date formats for consistent validation.
Builds the per-row errors and overall summary for the response.
Manages Set Default Rules and per-request overrides for multiple schemas.
Common scenarios where this AI agent adds value.
Practical answers to common concerns.
The agent validates JSON and CSV imports delivered via webhook. Data is expected as a data array within the payload and a corresponding rules object to define constraints. Rules support standard types and formats, including string, number, email, date, URL, and boolean. Date formats can be set to common patterns like YYYY-MM-DD, DD.MM.YYYY, or MM/DD/YYYY. The system is designed to handle large batches efficiently and to provide incremental error reporting for failed rows.
Rules are defined in a configurable rules object passed with the webhook payload. If a Set Default Rules node is present, those defaults apply when no rules are provided in the request. Request-specific rules override defaults for the current import, enabling multi-structure validation with a single endpoint. You can modify or extend the rules to fit evolving data schemas without changing the endpoint. The validation engine enforces the rules consistently across all records.
Yes. The agent is designed to operate in self-hosted or cloud environments and does not require external API keys. It works with standard webhook adapters and can be deployed inside your existing data pipeline. The rules and reporting operate identically in both modes. If you already have n8n or similar automation in place, you can route webhook data through this agent with minimal changes.
No external services or API keys are required for validation. Everything runs inside your environment or the cloud instance you control. The workflow relies on built-in rule checks and local aggregation to produce the report. This keeps data handling straightforward and compliant with internal security policies.
Yes. Rules sent in the request body override the default rules for that import, enabling flexible validation for multiple data structures. When defaults are explicit, the system continues to apply them where rules are absent. This design supports a single endpoint servicing varied schemas without code changes. The override behavior ensures consistent validation across environments while preserving a central governance model.
The report includes a totals summary (total, valid, invalid) and a detailed error list with row number, field name, failed rule, and error message. Each invalid row shows the specific fields that failed and the reason. The delivery format is ready for API consumption or direct import into downstream systems. The report also supports exporting to JSON for programmatic integration and auditing.
Cross-field and custom validations can be implemented by extending the validation logic in the Code node and adding additional rules. The Set Default Rules node can define common cross-field constraints, while per-request rules override only the relevant parts. You can chain checks, such as ensuring one field's value aligns with another field or with external reference data. This keeps complex validations maintainable and centralized within the AI agent.
Monitor webhook data, check each record against configurable rules, log results, and notify stakeholders with a structured validity report.
