Making Conversations Vernacular

Collect, Annotate and Transcribe Multi-lingual data for conversational AI
Conversational AI - Making Conversations Vernacular

Exceptional personalized experience with Conversational AI

Lack of quality training data related to conversational AI has been a bottleneck in its progress & adoption. Curating, annotating, and transcribing multi-lingual conversational datasets in large volumes with quality high enough to build AI capabilities has always been a time-consuming and expensive task that requires skilled data annotators from multiple specialized domains.

A conversational AI model needs data for two main reasons: to know what people are saying, and to know how to respond. With proper training, AI applications can communicate automatically with the users in a way that not only feels authentic but also eliminates the need to have a human operator. Popular examples of Conversational AI are Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa, and Google Home. They respond to commands automatically in human language to enable intuitive and impactful conversations. 

Deep Expertise in Conversational Data & Transcription

Conversational AI or Chatbots or Virtual / Digital Assistants are only as smart as the technology and data behind them. At shAIp, we offer you a broad set of diversified data to mimic conversations with real people that lets you bring your AI to life.

With our deep understanding of conversational AI, we help you build and localize AI-enabled speech models, with utmost precision with rich and structured datasets in multiple languages from all across the globe. We offer multi-lingual audio collection, transcription, and annotation services based on your requirement, while fully customizing desired intent, utterances, and demographic distribution.

Scripted speech collection

Spontaneous speech collection

Data Transcription

Data Aggregation from web

Data Labeling & Annotation

shAIp lets you accurately train your Conversational AI so it can:

  • Seamlessly talk, text, and chat across multiple channels. 
  • Learn from existing interactions in the form of chat, voice transcripts, transactions, etc.  and suggest & converse, based on these learnings.
  • Understand the intent behind human speech and remove ambiguity in understanding human language.
  • Interact with you on a one-on-one basis and can be trained to identify users and remember past conversations.

Conversational AI / Chatbots Use Case

Difference between Conversational AI & Chatbots

Chatbots are mainly natural language text responses that are based on rules that encourage canned, linear-driven interactions. In comparison to conversational AI, that are typically easy to build and navigate as per predefined workflows.

Conversational AIChatbots
Evergreen: Online 24/7Persistent: Online 24/7
Natural language processingNavigation-focused
Machine learning enhancementsIf/Then statements, no capacity for learning
Becomes more successful with the useHard-coded Logic
Compatible with 100’s of integrationsStatic Integrations to specific landing pages
Incorporates sentiment & emotionAbsent
Omni-channel presence – talk, text, & chatText & chat

Our Approach

Step 1: Conversational Data Collection

  • Team to create instances of conversational data between human & bot
  • Instance auto-upload on the platform using a web-based interface
  • Languages: 40+ Languages in multiple dialects

Conversational Data Collection
Annotation on shAIp’s Patented Platform

Step 2: Annotation on Shaip’s Patented Platform

  • ShAIp to annotate each conversation
  • Entities to be identified within conversations
  • Entity extraction & tagging to be performed
  • Sentiment analysis for the conversation
  • Categorize conversations as per user case study

Step 3: Data Delivery

  • JSON for conversational data collection & annotated entities to be auto-delivered.
  • Ex. Any cloud platform

Data Delivery

Data Collection & Annotation Workflow

Conversation Data Collection

Data collected from across the globe in different languages and dialects

QA of Collected Data

QA to audit conversational data collected & ensure 95%+ accuracy

NER Annotation & Sentiment Analysis

Entity extraction & tagging, Sentiment & Intent analysis

Data Annotation QA

QA after annotation, NER, sentiment analysis

CQA (10% Samples)

Ensuring conversation diversity & maintain annotation accuracy

Why partner with Us

Core Competency

We have the right expertise to provide accurate and unbiased data collection, transcription, and gold-standard annotation, which has led to AI development for some of the biggest technology companies in the world.


Leverage our AI-based platform and proprietary tools for workflow management.


A network of 7,000+ qualified contributors that can quickly be assigned to build your custom AI training data and scale up the services anytime and anywhere in the world.

Right Partners

We treat our customers like partners. Our team is highly responsive and work 24×7 to complete projects on time and on budget.


Our ability to adapt to changing requirements is unmatched in the industry and we can often provide data 5-10x faster than our competition.


Being a full-stack AI company, Shaip can source, scale, and deliver training data from across the world in multiple languages and dialects to meet your exact requirements.


We understand how confidential your data is – we give utmost importance to data security and privacy and are also certified to handle highly regulated sensitive data.

Success Stories

We have worked with the world’s leading brands to build their conversational AI.

BOT Training

Generated 10,000+ hrs. of conversations in multiple languages as per specifications

Digital Assistant Training

3,000+ linguists provided 1,000+ hours of audio/transcripts in 27 native languages

Utterance Data Collection

20,000+ hours of utterances collected from multiple geographies in 22+ languages

Insurance Chatbot Training

Created thousands of conversations with an average of 6 turns per conversations

Featured Customers

Empowering engineering teams to build world-leading AI products.
Clientele - Google Logo
Clientele - Microsoft Logo
Clientele - Amazon Logo


Google, Inc.


Creating clinical NLP is a critical task that requires tremendous domain expertise to solve. I can clearly see that you are several years ahead of Google in this area. I want to work with you and scale you.

Google, Inc.

Head of Engineering

My engineering team worked with shAIp’s team for 2+ years during the development of healthcare speech APIs. We have been impressed with their work done in healthcare-specific NLP and what they are able to achieve with complex datasets.

Getting Started with Conversational AI?

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