Core Services

Services Included in Our End-to-End Workflow

A step-by-step service flow that covers image, video, text, and multimodal datasets with production annotation standards.

Core service workflow steps

01. Data Collection & Intake

Collect and organize raw image, video, text, and document data with class-aware sampling and structured metadata capture.

02. Data Cleaning & Structuring

Remove duplicates, unusable frames, and noisy samples, then normalize labels and dataset structure before annotation starts.

03. Segmentation & Task Preparation

Split video into action-level clips and prepare annotation-ready segments for frame, sequence, and document workflows.

04. Annotation / Labeling Execution

Expert labeling across boxes, polygons, masks, keypoints, tracking IDs, OCR/text, classification, and attribute tagging.

05. Quality Check & Delivery

Multi-layer QA validation and export in COCO, YOLO, JSON, CSV, or custom schemas for reliable model training handoff.

06. After-Sales Support

Post-delivery support for relabeling, error corrections, class updates, and next-batch planning as your model and dataset evolve.

How We Work

Structured annotation workflow for production AI

01

Requirement Analysis

We understand model type, edge cases, and target performance.

02

Pilot Batch

Small batch delivery to align on quality standards.

03

Full-Scale Annotation

Dedicated team, QA review, structured workflow.

04

Delivery & Support

Formatted datasets plus ongoing refinement support.

Quality & Security

Built for Production AI

Technical delivery standards for ML teams that need repeatable annotation quality and secure handling.

Multi-layer Quality Assurance
NDA Friendly
Secure Data Handling
Consistent Labeling Standards
Scalable Dedicated Teams
Access-Controlled Project Workspaces
Start Your Project Talk to Our Team

Industries

Industries we support with computer vision datasets

Retail AI
Surveillance & Security
Autonomous Systems
Industrial Automation
Healthcare Imaging
Sports & Entertainment
Agriculture
E-Commerce
Media

Additional Capabilities

More detail on annotation, data cleaning, and delivery support

Explore platform support, output formats, and workflow details in a clear, production-ready structure.

AI and ML Network provides production-focused data services for AI teams that need reliable, model-ready datasets. Our work is designed for companies building computer vision systems, machine learning pipelines, and AI products where annotation quality affects model performance directly.

Data Collection

Collect high-quality raw data from cameras, video streams, documents, text sources, sensor logs, and existing datasets. We define capture and sampling guidelines so classes stay balanced and edge cases are represented.

Data Cleaning

Clean and structure datasets by removing duplicates, corrupt files, unusable frames, and inconsistent metadata. We standardize class names and dataset structure before annotation begins.

Segmentation

Split into action-level clips for video projects and create task-ready chunks for image, text, and document datasets. This makes long raw assets easier to annotate consistently.

Data Extraction

Extract relevant data and features such as objects, attributes, events, timestamps, text fields, and contextual metadata needed for model training and evaluation.

Annotation / Labeling

Precise labeling by experts across all major annotation types:

  • Bounding boxes
  • Polygon annotation
  • Semantic and instance segmentation masks
  • Keypoints and landmark tagging
  • Object tracking and ID continuity
  • OCR and text labeling
  • Classification and attribute tagging

Quality Check

Multi-level accuracy validation with guideline checks, reviewer layers, inter-annotator consistency checks, and final QA audits before export.

Delivery

Export in model-ready formats with schema validation, class-map checks, and structured handoff notes for training and retraining workflows.

Annotation in Existing Platforms

If your dataset is already uploaded, we can work directly in your preferred environment, including:

  • CVAT
  • Roboflow
  • Supervisely
  • Label Studio
  • Encord

Delivery Formats

We export datasets in the formats most AI teams already use:

  • JSON
  • COCO
  • YOLO
  • CSV
  • custom schema formats

Why Teams Choose AI and ML Network

Clients usually come to AI and ML Network for one reason: they need data they can trust. Our service model is built around annotation accuracy, QA discipline, export validation, and dependable handoff for AI training.

If you need a structured annotation partner, go to the Start Project page and send your requirements.