Tabularis offers an on‑premise, edge‑compatible platform that automates multi‑modal data labeling, synthetic data generation, and GDPR/HIPAA‑compliant PII detection for text, images, tabular, time‑series, and audio data. By combining foundation‑model pre‑labeling, active learning, and human‑in‑the‑loop review, it delivers training‑ready datasets in hours while keeping data fully sovereign and reducing cloud API costs by up to 90%.
Funding
Funding not disclosed
Founders
Product
Problem
AI projects in regulated and data‑sensitive environments stall because teams cannot access enough high‑quality training data or share raw records with cloud services without violating GDPR, HIPAA, or other compliance requirements. Manual labeling is slow, costly, and exposes sensitive information, while synthetic data generation often lacks realism or privacy guarantees.
Solution
Tabularis provides an on‑premise, edge‑compatible platform that automates data labeling, synthetic data generation, and PII detection for text, images, tabular, time‑series, and audio modalities. The system combines foundation‑model pre‑labeling, active‑learning uncertainty scoring, and a human‑in‑the‑loop review UI to produce training‑ready datasets in hours rather than weeks, with less than 5 % of items requiring manual review. Synthetic data modules model statistical structure, business rules, and rare edge cases to create realistic, privacy‑preserving datasets without using any real personal records. All processing can run offline within a VPC or on‑premise, ensuring zero latency, full data sovereignty, and up to 90 % cost reduction compared to cloud APIs. The platform also includes GDPR‑first PII detection and redaction for 42 personal data types across EU languages, keeping sensitive information out of analytics pipelines.
Target Audience
Primary customers are machine‑learning teams, data engineering groups, and compliance‑focused organizations in regulated industries such as finance, healthcare, and enterprise SaaS that need rapid, secure labeling and synthetic data generation.
Features
- Multi‑modal fast labeling pipeline (text, images, tables, time‑series, audio) with foundation‑model pre‑labeling and active learning, achieving 1000× throughput and >95 % label agreement
- Synthetic data generation for text, tabular, time‑series, and images that preserves statistical patterns while eliminating real personal records
- On‑premise, edge, and VPC deployment options with zero‑latency inference and full data sovereignty
- GDPR/HIPAA/SOC 2‑compliant PII detection and redaction for 42 data types in 24 EU languages, supporting batch and real‑time APIs
- Seamless integration with CSV/JSON, S3, GCS, Azure, BigQuery, Postgres, Kafka, and other data sources
- Automated quality assurance with consensus scoring, audit trails, and export to JSON, COCO, CSV, Parquet, or direct pipeline ingestion