From raw data to production-ready AI — on one platform.

Dataset Management + Labeling + Training + MLOps

Intellabel unifies data labeling, dataset management, model training, and MLOps in one workspace — so vision and multimodal AI teams ship faster, with the quality controls and audit trail regulators actually accept (Intellabel is ISO 27001 Certified by TÜV Rheinland)
AI-Assisted Labeling
Custom Model Training
MLOps
Book a demo
AI data operations dashboard showing annotation metrics, data sources breakdown, and dataset performance analytics with charts and task insights

Trusted by AI Teams Worldwide

CGS
Plumer AI
Knit
Sight data
Thingtrax
Protex AI
Axon vision
Caterra
Video verse
Arboair
Calipsa
Motorola solutions

One platform. Four capabilities.

01
Dataset Management
Ingest image and video data at scale. Versioning, tagging, splits, and full lineage from raw upload to deployed prediction.
02
AI-Assisted Labeling & QA
Foundation models pre-label every frame. Humans verify only the disagreements. Every reviewer, timestamp, and decision is logged — the audit trail builds itself.
03
Model Training
Versioned datasets, experiment tracking, and active learning. Reproducible training runs your MLEs will actually trust.
04
Production MLOps
Controlled rollouts, drift signals, and auto-generated model cards. Deploy vision and video AI with the documentation regulators ask for, generated as you go.
All Features

500,000 frames in S3 → 5,000 that matter. In minutes

Bring continuous video data. Intellabel's five sampling strategies automatically select the highest-value frames from your unlabeled pool. Label only what improves your model

Coreset

Selects a representative subset of data that preserves the overall distribution of the dataset while reducing labeling effort.

Dedupe

Detects and removes duplicate or near-duplicate samples to eliminate redundancy and improve dataset quality.

Hardcase

Identifies the most challenging or uncertain samples that require additional review or labeling to improve model performance.

Rarepick

Finds rare, unusual, or underrepresented samples to increase dataset diversity and improve edge-case coverage.

Spreadmax

Maximizes dataset diversity by selecting samples that are evenly distributed across the feature space.

Active Learning – Object Detection

Prioritizes unlabeled images with the most uncertain object detection predictions for efficient annotation.

Active Learning – Segmentation

Selects segmentation samples with the highest prediction uncertainty to improve mask quality and model accuracy.

Build models, not infrastructure.

Stop stitching annotation tools, dataset versioning, training, and deployment together. Intellabel does all four — with the same audit log running through every step.

Icon

Run pipelines without writing orchestration code

Icon

Deploy models without setting up infrastructure

Icon

Monitor models without building dashboards

Icon

Scale workloads without managing GPUs

Traditional MLOps Tools
  • Requires DevOps setup
  • Requires pipeline engineering
  • Requires cloud expertise
  • Multiple tools required
  • Weeks to production
Our MLOps
  • No infrastructure setup
  • Prebuilt pipeline workflows
  • Managed compute with credits
  • All-in-one platform
  • Deploy in hours

Skip the setup. Deploy your first pipeline in hours.

View Plans
View Plans
Who Intellabel is built for

ML and AI engineering teams that want to ship vision models faster, and heads of AI in regulated industries that need every dataset, label, and model decision to be defensible. Same platform. Same audit trail.

View Plans
View Plans

Built for AI that has to be trusted in production.

AI-Assisted Labeling + Quality Guardrails
Data-to-Model Pipeline (Train on Your Own Data)
Production-Ready MLOps for Vision & Video
AI-Assisted Labeling + Quality Guardrails
Foundation models pre-label every frame; humans verify only the disagreements. Every decision is logged with reviewer identity and timestamp — the audit trail builds itself.
Book a demo
Traffic scene with AI-assisted object detection and annotation on vehicles for data labeling.
Intellabel platform interface showing dataset, annotation, and review workflow stages.
Data-to-Model Pipeline
Versioned datasets, lineage from raw frame to deployed prediction, and reproducible training runs that satisfy auditors, regulators, and your own MLEs at 2 a.m.
Book a demo
Intellabel platform dashboard showing traffic image datasets organized by annotation status across a labeling task.
Intellabel MLOps dashboard listing YOLO model training pipeline runs with status, stack, and timestamps.
Production-Ready MLOps for Vision & Video
Controlled rollouts, drift signals, and automated model cards — deploy vision and video AI with the documentation regulators ask for, generated as you go.
Book a Demo
Intellabel annotation interface showing a busy traffic scene with labeling tools and frame timeline.
Intellabel MLOps artifacts panel listing versioned validation batch predictions and labels with creation timestamps.
Book a demo
Book a demo
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
One platform to turn raw data into production-ready AI: combining data annotation, dataset management, and machine learning workflows in a single system.

Address: WeWork, Salarpuria Symbiosis, Bannerghatta Rd, Bengaluru, Karnataka

HomeAboutFeatures
Pricing
ResourcesContact Us
Services
Image/Video Annotation
AI Consulting
Content Moderation
Privacy
Terms & Conditions
© Copyright 2026 | Design & Developed By Intellabel - License | Powered By Sunix AI