LabelFort is an audit-ready data annotation platform and managed annotation service from Predusk AI (Predusk Technology Pvt. Ltd., Jaipur, India), built for AI teams whose training data has to survive an audit. AI pre-labelling handles the mechanical work, structured human review catches what automation misses, and every dataset exports with its chain of custody attached. Teams can run the platform themselves, bring in a trained domain specialist team through Hire a Team, or combine both under one governance model.
Key Features and Functionality:
- Human control point on every workflow: Five QA workflows (single pass, maker checker, double-blind, AI review, and AI consensus), each ending in a human decision. Quality is measured through per-cohort inter-annotator agreement, not sampled.
- Evidence export, not a status report: Every pre-label is versioned, and every reviewer action is logged in an immutable audit trail. A dataset exports as an evidence pack that maps to EU AI Act, HIPAA, GDPR, SOC 2, and DPDP documentation requirements.
- Annotation types on one platform: Image, video, text, audio, LiDAR, DICOM, document annotation, RLHF, and synthetic data generation, supported by an annotation canvas, ontology and form builder, QA console, audit log viewer, model registry, and dataset manager.
- Eight roles with separate routing, dashboards, and access checks, so a customer can name who labelled, reviewed, and approved any record.
- Physical control layer for regulated projects: Annotation runs on a controlled on-premises floor in India. No phones, paper, or storage devices enter, entry and exit are logged by name against the project a person is cleared for, and no regulated work is done remotely.
- Independent ownership: LabelFort is not owned by a model lab or a competing AI developer, so customer data does not route through a company training its own models.
Primary Value and Solutions Provided:
LabelFort addresses a gap most annotation vendors leave open: they assert quality and security but cannot produce the record. LabelFort produces the record as a deliverable. Quality is measured per cohort, custody is logged per person and session, and the platform generates the evidence pack rather than reconstructing it at audit time. This serves healthcare and life sciences, autonomous vehicles and geospatial, public sector and defence, financial services, and robotics and embodied AI teams that must show a regulator or procurement reviewer who labelled what, under which guideline, and with what agreement score. Teams that only need low-cost volume labelling with no audit requirement will find the review depth heavier than they need.
