With the advent of new generation AI-enabled chatbots and virtual assistants, efficient and warm handling of basic queries, assistance replies to product/service oriented feedback is just the tip of the iceberg. An improved and robust customer experience requires these chatbots to hold the conversation for a little longer than just initial one-liners. Since chatbots and virtual assistants are limited in their scope, we help AI companies to train their customer service algorithms to be more intelligent and conversational with our NLP data annotation solutions.

NER Named Entity Recognition and Classification (NLP)

AIsmartz team leverages best-in-class platforms to detect named entities from semi-structured and unstructured text sources with a high degree of accuracy and classifies them into predefined categories. We have come across the usage of NER in medical, financial, and legal AI document applications.

Sentiment Analysis
(NLP)

We classify and label each pixel of an image for fine-grained understanding. It enables image classification with pixel-wise annotation to localize images with intense precision. AIsmartz team segments multiple types of objects in images belonging to a single class at a pixel level.

Intent, Entity, and Conversation Analysis
(NLP)

Chatbots, digital assistants, and customer service enhancement AI applications bank on data learning processes that are rich with labeled data highlighting user intent, topic classified content, and conversation quality indices.

Semantic Analysis
(NLP)

Identifying context and extracting relevant information from text structures with words with more than one meaning.

Text Tagging
(NLP)

Our experts meticulously annotate and classify the terms into their relevant groups and turn vast volumes of data into accurately tagged insights.

Data Annotation Workflow

Instructions Set

You share your sample data with business rules.

Task Analysis

Our annotation experts share their opinion on workforce hours and tools required for the job

Data Labelling with Checks

Once signed up, our teams work in close contact with you for initial 8 weeks to understand edge cases

Production Grade Annotation

QA Managers monitor throughput closely with gold standards, consensus and sampling

Exported Training Data Feedback

Finished training data run by you for feedback and info on model iterations

SUGGESTED READS

Choosing the right Data Annotation Tool

Since the performance of your ML model is as good as the data that trains it, understanding the tools used for annotating this data becomes especially important. These tools determine the quality of the data and can have implications on the success or failure of your model. … read more

Key Factors to find synergy with Labeling Partner

When we think about AI and Machine Learning, we naturally tend to think of self-driving cars, delivery drones, robot-assisted precision surgeries, and all the technological innovations that have been doing the rounds lately.… read more

Our Client's Speak

Use Cases

Finance and Insurance Tech

We partner with asset management, legal, finance and insurance firms to assist them to embark on their journey of robotic automation of gathering quick insights on lengthy legal, structural and financial data. … read more

Customer Service Automation

With the advent of new generation AI enabled chatbots and virtual assistants, efficient and warm handling of basic queries, assistance replies to product/service oriented feedback is just the tip of the iceberg… read more

Government

Be it e-auction sites, public tenders, taxation, documentary record repositories, or nature, animal, people & demographics information, database management and enrichment is an essential facet of building data authenticity and enhancing user experience... read more

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