Generate realistic synthetic documents, identity records, and images — entirely inside your environment. Redact sensitive data on-prem, synthesize what you need on-prem, and give AI teams production-grade training data without a single real record leaving your infrastructure.
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.
BIPA, GDPR Article 9, and the EU AI Act (August 2026) are making it legally risky to use real documents, identity data, and health records for AI training. The safe path is synthetic — but only if it's generated compliantly.
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.
Run Grit-Redact first to sanitize your production data — the clean output becomes GritMold's template. Your team designs realistic synthetic variations without ever sharing raw records with anyone, including us.
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.
Generate realistic synthetic documents, ID images, financial statements, medical images, and audio from templates — without using real data. The generation engine runs entirely inside your VPC, cluster, or on-prem machine. We pre-build and ship a generation model directly into your environment — model weights deploy once, then everything runs on your hardware. Your raw data never leaves.
On-prem generationDetect and redact PII/PHI/Custom entity types using a local NLP model deployed entirely inside your environment. No external APIs, no data transfer, no exposure outside your VPC, cluster, or machine. Model weights stay in your infrastructure — no external API calls, ever.
Privacy-firstGenerate synthetic data that preserves statistical distributions, inter-column relationships, and domain-specific patterns — validated against KS-test and inter-column correlation preservation across distribution types. Downstream models train and evaluate against data that behaves like the real thing.
Model-grade fidelityGenerate controlled volumes of rare but critical scenarios — fraudulent transaction patterns, OCR-edge handwriting, low-quality identity images, device failures, and outlier populations. Test against conditions that don't exist in production data.
Coverage you can't harvestOne 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.
The redaction engine runs a local NLP model inside your perimeter — no data transfer, no external API calls. The synthesis engine deploys as a pre-built model to your infrastructure — model weights ship once, then generation runs entirely on your hardware. Only sanitized templates transit between the two. Never actual records.
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.
As regulations tighten around biometric, health, and identity data, using real records for AI training is becoming legally complex. Synthetic data has no real-person attributes — compliant by construction, auditable by default.
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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