Takt Text

Your custom NLP Annotation Engine

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Use Cases

The training datasets your algorithms require are built on a vast corpus of text-based data. You need to able to understand and gain insights from your data in the form your algorithm requires. Takt Text handles a multitude of solutions with the ability to customize to your specifications. Talk to Sales Engineering

Named Entity Recognition

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Build complex information extraction applications, in fields such as Clinical AI, that rely on Named Entity Recognition by domain experts who understand the context

Text Classification

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Leverage Takt's customized workflows, implemented through our platform, to handle complex text classification tasks that cannot be crowdsourced.

Multi-Lingual Transcription

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Utilize Takt's data centers in India and South-East Asia to service a range of languages while maintaining the accuracy through in-house domain experts.

Data Collection

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Build diverse corpuses for your unique applications by leveraging our on-demand and scalable workforce for your data collection requirements.

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How TAKT excels at Text annotation

Dataset-Annotator Fit

In-house Domain Experts

High quality annotation depends hugely on Dataset-Annotator fit. Our in-house hiring of full-time annotators is built to optimize for domain experts who understand the nuances of your dataset.

  • Rapid Hiring Mechanism
  • Dataset Testing
  • Skill Matrix Tracking
  • Bias Elimination

Consistency at Scale

Constant Review & Repeatable Training

Consistency requires different annotators to make the same decisions across time. Our methodologies train annotators to think from the perspective of the algorithm which results in consistent decision making.

  • Regular training and Q/A sessions
  • Concurrent Annotation
  • Feedback Loops
  • Shared Slack Channel for instant clarifications

Predictable Efficiency

Efficient Annotators = Optimal Timelines

Takt's platform, used by full-time annotators at our secure data centers, implements optimal workflows. Live efficiency metrics at our fingertips enable us to stay in complete control of the annotation task.

  • Platform implemented workflows
  • Flagging systems to spot inefficiencies
  • Average Time / Annotation
  • Confusion Matrices from Review Process

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Live Task Progress

Real-time metrics on the progress of your annotation task

Doubt Pool

Flagged doubts to prevent low confidence annotations going unchecked

Instant Feedback Loop

Internal feedback mechanisms to clarify doubts rapidly

Multi Lingual Proficiency

Diverse data centers to support multiple languages and domain expertise

Confidence-Based Consensus

Multiple variations of consensus to eliminate the bias from your dataset

UI Flexibility

Ability to leverage Takt annotators for your in-house annotation UI

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Conversational AI

Develop chatbots and virtual assistants on training datasets built by annotators who understand the context of the application

Clinical AI

Leverage the medical professionals in Takt's workforce to annotate clinical notes and sensitive medical information at high-quality

Social Insights

Identify trends on platforms like Facebook, Twitter, Spotify Podcasts and LinkedIn with carefully curated and annotated datasets

Information Extraction

Build corpuses, with no false positives, to automatically extract structured information from unstructured and semi-structured documents

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Every annotation task has its own unique requirement of domain experts, consensus protocols and annotation workflows. Fixed pricing models do not make sense given the variable components. At Takt, we offer custom pricing which factors in the complexity of your dataset, the type of annotation and the timeline of the task.

Here are a few examples of our pricing.

Contact Sales