iMerit vs. Kili Technology: Comparing Top Data Labeling Vendors

1. iMerit vs. Kili Technology: Company Profiles
   1. iMerit Company
   2. Kili Technology Company
2. Services and Products
   1. iMerit Services and Products
   2. Kili Services and Products
3. Pricing Models
4. Dataset Types
   1. iMerit Dataset Types
   2. Kili Technology Dataset Types
5. Data Annotation Tools
   1. iMerit Annotation Tools
   2. Kili Technology Annotation Tools
6. Integrations
   1. iMerit Integrations
   2. Kili Technology Integrations
7. Annotation Process
   1. iMerit Annotation Process
   2. Kili Technology Annotation Process
8. Quality Assurance
   1. iMerit QA
   2. Kili Technology QA
9. Security and Data Compliance
10. TL;DR
11. FAQ

If you’ve been struggling with inconsistent data quality, scalability issues, or unreliable vendors, you’re likely searching for a top-notch data labeling partner. After thorough research, you’ve narrowed it down to iMerit and Kili Technology data labeling companies.

This article is your go-to guide, providing clear and concise comparisons of their services, quality, scalability, pricing options, and more. Read on to make an informed decision and find the perfect partner for your machine learning project.

iMerit vs. Kili Technology: Company Profiles

iMerit Company

Founded in 2012 by Dipak Basu, iMerit has over 5000 employees and offers comprehensive data annotation services across various industries. Headquartered in San Jose, California, it also has offices in New Orleans, Kolkata, and Bengaluru, India.

Kili Technology Company

Founded in 2018 by Edouard d’Archimbaud and François-Xavier Leduc, Kili Technology is designed for data scientists and engineers to efficiently label large datasets for ML model training. Launched in 2020, it serves major corporations like L'Oréal, Renault, and Airbus for various AI applications, including facial recognition, autonomous driving, and predictive maintenance.

Services and Products

iMerit Services and Products

iMerit specializes in data labeling services for generative AI, computer vision, NLP, and content services. Their key focus areas include:

• Reinforcement learning from human feedback (RLHF) for LLMS and LVMS

• Image, video, and text annotation

• Audio transcription

• LiDAR annotation

• Sentiment analysis

• Content moderation

• Product categorization

• Image segmentation

Their annotators are multilingual, with a majority speaking English and based in India. Additionally, they have teams in the US, Bhutan, Germany, and Latin America, capable of handling multilingual ML projects.

Kili Services and Products

Kili Technology offers a comprehensive data labeling platform and expert services to help businesses build high-quality ML datasets.

Kili Data Labeling Platform:

• Supports text, document, image, and video labeling

• Includes AI-assisted tools for efficiency

• Features quality management and integration capabilities

Managed Expert Labeling Service:

• Global network of experienced annotators

• Provides quality control, customizable annotation, and real-time oversight

• Offers ML expert guidance and consulting services

Kili’s Products:

• davinci: An AI tool that accelerates patent drafting and office action responses using generative AI technology.

Pricing Models

Dataset Types

iMerit Dataset Types

iMerit handles image and video datasets for computer vision tasks like object detection, image segmentation, and image classification, essential for autonomous vehicles and facial recognition systems. They also manage text and document datasets, offering services such as NLP, sentiment analysis, and entity recognition. Additionally, iMerit works with audio datasets for speech recognition and transcription tasks.

Kili Technology Dataset Types

Kili Technology supports annotating various unstructured data formats, including images, videos, text documents, PDFs, satellite imagery, and conversational data. It provides dedicated interfaces for efficient labeling and supports LLM evaluation, supervised fine-tuning, and RLHF workflows. Data can be imported (CSV, JSON, image files) and exported (CSV, JSON, TensorFlow Record). Limitations include video annotation (bounding boxes only) and OCR integration.

Data Annotation Tools

iMerit Annotation Tools

For most annotations, iMerit experts use their proprietary tool, Ango Hub, for image, video, and text annotation. While they can work with clients' specific tools, they prefer using Ango Hub in all cases. Additionally, iMerit technology is available by subscription for individual annotation needs.

Kili Technology Annotation Tools

Kili Technology’s platform facilitates high-quality data labeling with AI-assisted automation, allowing adjustments to pre-labeled data. Users can employ their own custom models or utilize built-in AI models like ChatGPT and SAM for automatic pre-labeling, saving time on large datasets.

The platform supports various data types with tools for text, image, video, OCR, and geospatial annotation. However, some automated features are still developing, and complex tasks may require human annotators. Additionally, Kili's focus on its own tools might pose integration challenges with existing systems.

Integrations

iMerit Integrations

The data labeling company offers no-code integrations, with most deployments happening through APIs and plugins. They can integrate various applications and MLOps platforms, ensuring efficient data pipeline integration even for custom requests.

Kili Technology Integrations

Kili Technology provides tools to enhance data quality, collaboration, and integration with ML workflows. It offers API and Python SDK for programmatic access, supporting major data storage solutions like Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage.

This allows seamless data import/export and integration into ML pipelines. Additionally, Kili supports version control, simplifies labeling operations, and integrates with existing ML stacks and datasets. Note that full functionality requires webhooks, API access, and Python SDK, which may be complex for non-technical users.

Annotation Process

iMerit Annotation Process

iMerit emphasizes that human annotation is faster and more efficient, despite offering machine-assisted labeling for specific cases. Their main steps for data annotation include:

• Consultation with an expert

• Trial and annotators’ training

• Workflow customization

• Feedback cycle

• Evaluation

You can be involved in the annotation process at any stage you choose.

Kili Technology Annotation Process

The company provides a variety of services and approaches to data labeling for machine learning, which depends on the specific service you choose:

Platform-Based Annotation

• Easy Start: User-friendly platform with customizable interfaces.

• Pre-Annotation: Utilize your custom models for initial labeling.

• Job Types: Supports classification, detection, relation, and transcription.

• Quality Assurance: Integrated quality metrics and detailed reports to ensure high accuracy.

• Integration: Compatible with Amazon, Google, and Microsoft cloud storage.

Kili Simple Annotation Process

• Upload Data: Submit a sample with your guidelines.

• Quick Start: Receive a transparent quote and start promptly.

• Progress Monitoring: Real-time tracking of labeling progress.

• Guaranteed Accuracy: Delivered datasets with a 95% accuracy guarantee, with re-annotation offered if needed.

ML Expert Guidance

• Detailed Requirements: Share your project specifics and workflows.

• Iterative Feedback: Review and refine through calibration batches.

• Active Learning: Enhance data quality continuously with integrated model feedback.

Kili Technology ensures efficient, accurate, and scalable data labeling for all project complexities.

Quality Assurance

iMerit QA

During every step of data annotation, iMerit uses various reports, dashboards, and tracking systems to ensure efficient project management, identify troubleshooting needs, and track KPI metrics. To guarantee high-quality annotation, they employ several techniques:

• Setting a gold standard during mini-sets

• Using annotator consensus

• Applying scientific methods for label consistency

• Subsampling

The solution architect randomly selects a sample of the dataset, around 5-10% of the total labeled data, to check for errors. AI-based frameworks assist annotators in reviewing labeled datasets multiple times before project submission.

Kili Technology QA

Ensuring annotation quality is vital for successful data projects. Kili Technology provides robust QA tools:

• Consistency Checks: Automated reviews to identify and resolve discrepancies.

• Review and Feedback: Iterative feedback loop for continuous improvement.

• Quality Metrics: Metrics like inter-annotator agreement and annotation speed to monitor performance.

Kili guarantees 95% accuracy, offering:

• Low-Error Datasets: Reliable and representative data.

• Data Cleaning: Addressing quality, bias, and imbalance issues.

• Programmatic QA: Automated error detection and active learning for high-quality annotations.

For optimal results:

• Continuous Feedback: Ongoing team collaboration.

• Targeted Reviews: Random and specific data checks.

• Quality Metrics Integration: Tracking progress and improvements.

Security and Data Compliance

TL;DR

To sum up, Kili Technology is ideal for businesses seeking a robust, AI-assisted data labeling platform with seamless integration into existing ML workflows. iMerit, with its extensive end-to-end data annotation services, is better suited for large-scale projects requiring diverse annotation types across various industries.

And if you’re looking for a vendor with these qualities:

• No commitment

• Flexible pricing

• Tool-agnostic

• Data-compliant

Try our free labeling pilot to experience our service firsthand and see if we’re a match.

FAQ

by Yuliia Kniazieva
on June 13, 2024. Share on LinkedIn Post on Twitter