Sanitize existing data, generate synthetic data where needed, and expand coverage for realistic testing and evaluation — without exposing sensitive information.
Enterprise data is locked behind privacy, compliance, and internal controls. Teams either wait weeks for approval or move forward with weak, unrealistic datasets that fail to reflect production.
Privacy obligations, compliance requirements, and internal governance make production data slow to access and risky to use. Approval cycles drag on, and teams are left waiting.
Handmade or low-fidelity mock data misses the complexity, variation, and edge cases found in real enterprise workflows. What looks good in testing often fails in production.
Teams lose time, models fail under real conditions, and deployment slows down. Instead of moving faster, teams spend cycles compensating for weak data foundations.
Choose the approach that fits your current data reality — sanitize existing data, generate synthetic data from scratch, or expand coverage with edge cases and scenario variation.
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Detect and redact sensitive information, optionally replace with realistic values, and make it safe to use across teams and environments.
Create realistic synthetic datasets from scratch, tailored to your specific workflows, domains, and data structures.
Use sanitized data to generate edge cases, anomalies, and enterprise-specific scenarios to stress-test and improve your models.
Every capability is built around a core enterprise requirement: sensitive data stays inside your environment, while teams get safe access to usable, high-fidelity data.
Detect and redact PII/PHI/Custom entity types using a local model that runs entirely inside your environment. No external APIs, no data transfer, and no exposure outside your VPC, cluster, or machine.
Privacy-firstGenerate synthetic data that preserves statistical distributions, inter-column relationships, and domain-specific patterns, so downstream models can train and evaluate against data that behaves like the real thing.
Model-grade fidelityCreate controlled volumes of rare but critical scenarios — from fraud patterns to device failures to outlier populations — so teams can test against conditions that are hard to find in production data.
Coverage you can't harvestCreate realistic data across modalities — from structured records and documents to images and audio — so your teams can work with datasets that reflect how enterprise data actually appears in production.
Beyond tabular dataOne platform supports the full data lifecycle — from sensitive source data to clean, validated outputs ready for development, testing, and evaluation.
KYC files, claims documents, financial statements, identity records, and other workflow-critical business documents.
Scanned documents, ID images, and mixed-quality visual inputs commonly found in enterprise workflows.
Call recordings, interview audio, support conversations, and paired transcript files for speech and language workflows.
Tabular datasets, annotations, labels, and schema-bound exports ready for downstream systems and model pipelines.
Give teams safe access to realistic, representative datasets for model development, analytics, and experimentation — without waiting on production approvals.
Validate systems against realistic scenarios, improve coverage, and stand up testing environments faster with data that mirrors production conditions.
Keep sensitive data inside your environment while giving internal teams access to usable data for development, testing, and analysis — with stronger privacy and governance controls.
No data leaves your infrastructure. GritWorks runs inside your perimeter, so teams can work within the compliance, privacy, and security boundaries you already have in place.
One tool covers all your data provisioning needs — redact existing data, generate synthetic from scratch, or do both in the same workflow.
Work with data that reflects the structure, variability, and complexity of real enterprise workflows — not lightweight examples or generic test fixtures.
Generate anomalies, rare scenarios, and adversarial examples your models need to handle before they reach production.
Built with regulated environments in mind, so finance, healthcare, insurance, and other high-compliance teams can move faster with less friction across security and governance reviews.
Give your teams safe, usable data in days — not weeks. GritWorks helps you sanitize what exists, generate what is missing, and move faster without exposing sensitive information.
Give your AI and data teams safe, realistic datasets for development, testing, and analytics — without waiting weeks for access or exposing sensitive information.
Perspectives on synthetic data, privacy-preserving ML, and enterprise AI development.
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