Custom AI agent · Documents & Data
AI Data Validation Agent
Catch bad data before it reaches the next system.
Check records against your data contract, explain failures, and route exceptions before they reach downstream workflows.
- Fewer invalid records in downstream workflows
- Consistent checks against business rules
- Clear exceptions for owners to resolve
Starting at $300 USD/month
Starting price is $300 USD/month for a tailored solution. Your final proposal defines the scope, deliverables and payment terms. Data volume, rule complexity, connections, hosting, alerting, monitoring and ongoing support are scoped and priced before work begins.
What is an AI data validation agent?
An AI data validation agent checks incoming or existing records against the rules your team has agreed to use. It can evaluate required fields, formats, ranges, relationships, code lists, cross-field logic, and source-specific conditions, then explain what failed and route the exception to the right owner. Tranki configures the checks around your data contract and operating process. It can identify quality issues and prepare a correction path, but it does not invent values, silently discard records, or replace the accountable person who decides how an exception should be resolved.
What it can check
Validate field-level rules
Check required values, types, formats, ranges, patterns, enumerations, and permitted nulls against the data contract defined for each record type.
Test relationships and dependencies
Look for invalid references, broken keys, duplicate identifiers, missing parent records, and cross-field combinations that violate an approved rule.
Explain and prioritize failures
Describe the failed rule, affected field or record, source context, severity, and suggested owner so teams can distinguish a fixable issue from an intentional exception.
Route exceptions for resolution
Send validation results to the configured queue, workflow, or review process with the evidence needed to correct, accept, defer, or reprocess the record.
Where it can help
Inbound operational data
Check files, forms, API payloads, or system events before they enter customer, order, inventory, finance, or service workflows.
Warehouse and reporting feeds
Validate scheduled loads for missing dimensions, unexpected values, stale records, broken relationships, and schema changes before dashboards refresh.
Controlled data-quality programs
Run repeatable checks across critical datasets, track recurring failures, and give stewards a practical queue for investigation and correction.
How it works
A step-by-step workflow your team can review before launch.
Define the data contract
Tranki maps the record types, required fields, allowed values, relationships, severity levels, source owners, and exception policy for the workflow.
Receive and profile the data
The agent reads approved inputs, checks schema and sample patterns, and separates normal variation from conditions that violate the agreed rules.
Run validation and explain failures
Each issue is linked to a specific rule and record context, with severity and evidence rather than a generic pass or fail message.
Route, correct, and recheck
Owners resolve or approve exceptions through the configured process. Corrected records can be revalidated before the next system accepts them.
Built around your business
What we configure
- Record types, field rules, formats, ranges, code lists, null handling, and cross-field dependencies
- Source-specific contracts, freshness thresholds, severity levels, tolerances, and intentional exception rules
- Review queues, data owners, correction workflows, alert channels, reprocessing, and audit history
- Approved file, API, database, warehouse, CRM, ERP, form, or event-stream connections
What we need from you
- A current data dictionary or sample records for each dataset in scope
- Named data owners who can define valid values, tolerances, intentional exceptions, and remediation actions
- Approved access to the source and destination systems, with privacy, retention, and sensitive-field rules
- A safe process for correcting or quarantining records and re-running validation
Clear boundaries before launch
What it will not do
The agent does not invent missing values, approve a record solely because it looks plausible, delete data, or change a business rule without authorization. A failed check may be an intentional exception, while a passing check does not prove that the underlying information is true. High-impact, regulated, financial, identity, or safety-related records require qualified review and an approved correction or quarantine process.
How success is measured
Track validation pass rate by dataset, failure rate by rule, recurring issue categories, time to resolution, reprocessing success, false-positive exceptions, stale-data incidents, and downstream corrections. Review whether the checks surface meaningful defects early without creating noise that causes owners to ignore them.
Frequently asked questions
What does an AI data validation agent do?
It checks approved records against field, relationship, format, freshness, and business rules, then explains failures and routes exceptions. The exact validation contract is defined with your team.
Can it correct invalid data automatically?
It can prepare or trigger a correction workflow when the action is explicitly scoped and authorized. It should not invent values or overwrite source data without an approved rule and review path.
What kinds of rules can it check?
Common checks include required fields, types, formats, ranges, code lists, dates, references, uniqueness, freshness, cross-field logic, and source-specific tolerances.
How does it handle intentional exceptions?
Your team can define exception categories, approval roles, and expiration or review rules. A valid exception should be recorded with its owner and reason rather than hidden as a pass.
Can it validate data before an import or API call?
Yes. A pre-ingestion workflow can inspect files, forms, API payloads, or events and route failures before the downstream system accepts them, subject to the approved connection and scope.
Can it monitor recurring data-quality problems?
Yes. The workflow can group failures by rule, source, owner, dataset, and time period so the team can address recurring causes instead of treating every record as an isolated issue.
What is included in the $300/month starting price?
The starting price covers a scoped custom solution baseline. Your proposal defines the datasets, rules, volumes, connections, correction workflow, hosting, monitoring, and support included.
