LabelFort is Predusk AI's data annotation and training platform for regulated teams. AI-assisted labelling, verified by trained human reviewers, with the audit trail your Compliance, Legal, and Procurement teams already expect to see. Hardened on 100,000+ real images before we ever shipped it.
Five frameworks. One annotation backbone that passes Legal, Security, and Procurement.
Combining advanced automation with human expertise to deliver high-fidelity datasets, meticulously optimised for the most demanding production environments.
Enhanced precision and reliability for vision-based models.
Robust datasets for stable and accurate language models.
Seamless continuity and better recall for dynamic events.
Meticulous accuracy for advanced voice interfaces.
Zero manual re-entry on regulated document pipelines.
Production-scale perception stacks that survive a safety case.
Clean, reviewable datasets ready for ML pipelines.
One dataset, one audit trail, across every modality you ship.
Most annotation tools were built to move fast. LabelFort was built to move defensibly. When your regulator asks who saw the data and how you know the answer is right, you produce the evidence in an afternoon.
Every label, reviewer action, and configuration change is captured immutably. Export the full chain-of-custody for any project, in formats your auditors already accept.
Role-based workspaces for Operations, Quality, and Client. Hierarchy-based projects enforcing purpose limitation. Hardened access controls on every verifier surface.
Pre-label with our Visual Language Model pipeline. Route everything through structured human review. The speed of automation, the defensibility of human-in-the-loop.
Pre-labelling with our Visual Language Model pipeline removes the mechanical work. Structured human review removes the risk that auto-labels slip through. Every pre-label is versioned, every reviewer action is logged, and every dataset exports with the chain-of-custody attached.
Image · video · LiDAR
audio · documents
Hash & classify
Text, tables, geometry
drafted automatically
AI-ASSISTED · 30–60% TIME CUT
Annotator corrects,
reviewer verifies
HUMAN-VERIFIED · NON-NEGOTIABLE
IAA scoring · κ & α
cohort-level sign-off
THRESHOLD: κ > 0.90
COCO · YOLO · KITTI
+ audit log + IAA
Chain-of-custody attached
Image · video · LiDAR
audio · documents
Hash & classify
Text, tables, geometry
drafted automatically
AI-ASSISTED · 30–60% TIME CUT
Annotator corrects,
reviewer verifies
HUMAN-VERIFIED · NON-NEGOTIABLE
IAA scoring · κ & α
cohort-level sign-off
THRESHOLD: κ > 0.90
COCO · YOLO · KITTI
+ audit log + IAA
Chain-of-custody attached
We build for the industries where training data has to stand up to an external review — clinical, financial, safety-critical, and regulated by design. Every vertical below runs on the same governance primitives.
Radiology (X-ray, MRI, CT), pathology whole-slide imaging, surgical video, and claims extraction — with HIPAA-aligned controls and pathologist-in-the-loop QA.
Medical imaging · Claims extraction · Clinical NLP
Transaction labelling, document extraction for KYC/AML, and fraud-detection datasets with evidence trails that satisfy internal model-risk and external audit.
Fraud detection · KYC/AML · Transaction tagging
3D cuboids, point-cloud segmentation, cross-frame object tracking, and MOT17-ready exports for perception stacks that have to survive a safety case.
LiDAR & 3D · Tracking · Sensor fusion
Reusable skeleton schemas, pose-aware metadata, and video tracking pipelines for manipulation, teleoperation, and humanoid training data.
Keypoint/pose · Action labels · Sim-to-real
Our Visual Language Model pipeline extracts text, tables, and structured fields from documents and images, then routes to structured review. No manual re-entry.
Contracts · Invoices · Forms
Structured workflows for RLHF preference collection, red-team evaluation, instruction tuning, and model output audit — with IAA reporting on every cohort.
RLHF · Evaluations · Red-team
Product tagging, attribute extraction, catalogue enrichment, and audio labelling (transcription, speaker ID, sound events) for search, recommendation, and voice.
Product tagging · Speech · Sentiment
Geospatial imagery, aerial video, and policy-grade document analysis — delivered under the data-residency and access controls procurement actually requires.
GEOINT · Aerial video · Records
For ML teams inside regulated enterprises: the annotation backbone that passes Legal, Security, and Procurement on the way to production.
Internal ML · Governance · MLOpsWe don't run open trials or price-per-label comparisons. We run an evidence-grade motion designed to put your Compliance, Legal, and Security teams in the room from day one — then move to production with the paper trail intact.
We start with your risk surface, not our feature list. Data classification, residency, access model, and evidence expectations are mapped before any data moves.
A scoped pilot on your real data, under your real constraints. Success is measured on IAA, SLA adherence, and audit coverage — not on volume delivered.
Ramp to production volume with role-based workspaces for Operations, Quality, and Client. Hierarchy-based projects enforce purpose limitation by configuration.
Production delivery with the evidence pack your internal reviewers already signed off. COCO, YOLO, KITTI, MOT17, JSONL — delivered to S3, GCS, or your pipeline.
The ones Compliance, Legal, and Security raise on the first call. We've written the answers down so the conversation can start where it matters.
Yes. LabelFort is certified against ISO 27001:2023, aligned to SOC 2 Trust Services Criteria, and engineered to satisfy HIPAA, GDPR, and India's Digital Personal Data Protection Act (DPDP).
DPDP readiness is not an add-on — it inherits from the already-audited ISO and SOC 2 baseline. A control-mapping document and our DPDP readiness report are available for Legal and Compliance review during the intake.
LabelFort is a multi-modal platform covering image (bounding boxes, polygons, semantic and instance segmentation, keypoints, skeleton-based pose), video (object tracking, action recognition, temporal tagging, AI-powered tracking workflows), LiDAR and 3D point cloud (cuboids, segmentation, cross-frame tracking), text (NER, classification, sentiment, intent/slot), and audio (transcription, speaker ID, sound events, sentiment).
Standard exports include COCO, YOLO, KITTI, MOT17, Pascal VOC, JSON, CSV, and JSONL.
Quality is measured using Inter-Annotator Agreement, scored with Cohen's Kappa and Krippendorff's Alpha. The proof threshold is greater than 90%, validated on your dataset during PoC and tracked in delivery reporting.
Every annotation action, review, and configuration change is captured in an immutable audit trail and exports as a chain-of-custody record for any project. Audit-log coverage is 100% by design, and compliance breaches are a non-negotiable stop metric.
LabelFort ships with dedicated role-based workspaces for Platform Admin, Organization Admin, Operations Manager, Quality Manager, Annotator, Reviewer, Auditor, and Client. Each role has its own routing, dashboards, and navigation.
Access checks are enforced on protected verifier and auditor pages, and platform admins manage users in the context of a selected organization — so multi-organization governance is clean and inspectable.
Yes. LabelFort is tool-agnostic and designed to slot into the pipeline you already have. Exports cover COCO, YOLO, KITTI, MOT17, Pascal VOC, JSON, CSV, and JSONL. Delivery is via S3, GCS, APIs, or webhooks.
File-dimension and tracking-attribute integrity are preserved end-to-end, so geometric and temporal annotations do not drift relative to the underlying media during export.
The standard motion is Compliance Review → evidence-grade PoC → governed pilot → procurement-ready scale. Pilot-to-production typically runs 30–45 days once the PoC evidence pack is signed off by your internal reviewers.
We do not run open trials, we do not compete on price-per-label, and we do not enter engagements without a defined evidence output for Legal and Procurement review.
Yes. LabelFort supports structured RLHF preference collection, instruction tuning, evaluation rubrics, and red-team workflows for foundation-model teams — with the same IAA reporting, role-based QA, and audit coverage as every other project type.
Yes. Predusk Technology Pvt Ltd is an independent company with no hyperscaler or model-lab ownership stake. Your training data, audit logs, and evidence packs do not route through, or back to, any competing foundation-model lab. This has become a reasonable question to ask annotation vendors in 2026, and we're structured to answer it straight.
Request a demo through the Book a Demo flow. We'll scope the risk surface, map controls to your regulatory posture, and propose a PoC with defined evidence outputs — before any data moves.
Three forces converged over the last twelve months. Vendor neutrality became a procurement criterion. Auditable training-data provenance became a statutory requirement. AI-assisted pre-labelling went from optional to table stakes. LabelFort was built for this moment — not retrofitted for it.
High-risk AI systems must automatically log events in a tamper-resistant, traceable way. Penalties reach €35M or 7% of global turnover.
→ LabelFort ships with it by defaultData Fiduciaries face fines up to ₹250 crore. DPDP is enforced through outcomes — evidence that personal data was governed across the stack.
→ LabelFort inherits DPDP from its audited baselineAfter the Meta–Scale AI arrangement, Google, OpenAI, and xAI actively diversified away. Enterprise buyers now ask which model lab controls their data pipeline.
→ LabelFort is independent and stays that wayStart with a Compliance Review. We'll map your risk surface to LabelFort's controls and scope an evidence-grade PoC — on your data, under your constraints.
Request a DemoAudit-ready annotation for regulated AI.
LabelFort →End-to-end document processing for regulated workflows. Extract, classify, and verify at scale.
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