The Complete Guide to Conversational AI
The Ultimate Buyers Guide 2022
No one these days stops to ask when the last time you spoke to a chatbot or a virtual assistant was? Instead, machines have been playing our favorite song, quickly identifying a local Chinese place that delivers to your address and handles requests in the middle of the night – with ease.
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The global conversational AI market was valued at $6.8 billion in 2021. It is projected to grow to $18.4 billion by 2026 at a CAGR of 21.8%. Initially developed as an entertaining pet, conversational AI has grown phenomenally over the years.
Although conversational AI has become a part of the digital ecosystem, there is a lack of awareness among users – 63% of the users are unaware that they are already using AI in their daily lives. However, the lack of understanding hasn’t deterred people from using these Conversational AI systems. Chatbots are probably the most popular examples of conversational AI, and they are projected to witness a 100% increase in adoption during the next 2 – 5 years.
In a Gartner survey, many businesses identified chatbots as the primary AI application used by their organization. And that by 2022, nearly 70% of the white-collar workers will be interacting with conversational virtual platforms for their daily work.
Let’s look at the types of conversational AI and why it is gaining tremendous importance in the larger technological spectrum.
Who is this Guide for?
This extensive guide is for:
- All you entrepreneurs and solopreneurs who are crunching a massive amounts of data regularly
- AI and machine learning or professionals who are getting started with process optimization techniques
- Project managers who intend to implement a quicker time-to-market for their AI models or AI-driven products
- And tech enthusiasts who like to get into the details of the layers involved in AI processes.
What is Conversational AI
A programmatic and intelligent way of offering a conversational experience to mimic conversations with real people, through digital and telecommunication technologies.
Source: Deloitte: Digital Age Conversational AI
Conversational artificial intelligence (AI) or chatbots or virtual assistants or digital assistants are technologies that enable people and computers to effectively communicate through text or speech. Large volumes of audio and text data are used to train ML and NLP models that help in imitating human conversations while recognizing human speech or text patterns, identifying their intent and meaning across different languages.
Types of Conversational AI
Conversational AIs deliver different benefits to businesses depending on the need and design. Therefore, before developing a particular type of chatbot or virtual assistant, it is essential to understand the kinds of Conversational AI presently in use.
Choosing the suitable model depends mainly on your business goals. For example, suppose you are developing a retail chatbot. In that case, you might do well with an AI or Hybrid type since the chatbots have to interact with users, identify intent, and provide guidance for their shopping.
On the other hand, if you are developing FAQ chatbots, a rule-based algorithm can work well. The three major types of Conversational AI are Rule-based, Artificial intelligence, and Hybrids. Let’s look at each one in detail.
Also referred to as decision-tree bots, the rule-based chatbots follow a predefined rule. Following a decision-tree type of conversation structure, the chatbot maps out the entire conversation in a flowchart using a series of rules that help the chatbot solve specific problems. Since the rules form the basis for the problems and solutions the chatbot is familiar with, it anticipates the questions and provides pre-set responses.
The series of rules can be simple or complicated. However, the chatbot is not equipped to answer queries beyond the scope of the rules. These chatbots can only answer questions that fit into the trained scenarios.
Training a rule-based chatbot is easier, faster, and simpler to integrate with legacy systems. However, these chatbots cannot learn through interactions, limiting their scope for personalization and flexibility.
As the name suggests, AI chatbots use machine learning and natural language processing to understand the context and intent of the user before responding. AI-powered chatbots can formulate even complex natural language responses based on user questions.
With their intent and context understanding capabilities, AI chatbots can cater to the complex questions of users and customize the conversation based on user needs.
It might take longer to train AI chatbots than Rule-based chatbots, but they deliver highly reliable and customized responses once they are trained.
AI chatbots provide enhanced user experience by learning from previous interactions, understanding user behavior and drawing patterns, and comprehending different languages using advanced decision-making skills.
Difference between AI & Rule-Based Chatbot
|AI/NLP Chatbot||Rule-Based Chatbot|
|Understands and interacts with Voice and Text commands||Understands and interacts with text commands only|
|Can understand the context and interpret intent in a conversation||Can follow predetermined chat flow it has been trained on|
|Designed to have conversational dialogues||Designed to be purely navigational|
|Works on multiple interfaces such as blogs and virtual assistants||Works as a chat support interface only|
|Can learn from interactions, conversations||It follows a predesigned set of rules and has to be configured with new updates|
|Requires tons of time, data, and resources to train||Faster and less expensive to train|
|Can provide customized responses based on the interactions||Carries out predictable tasks|
|Ideal for complex projects that need advanced decision making||Ideal for more straightforward and well-defined use cases|
The hybrid chatbots use NLP and Rule-based algorithms to provide specific responses to user queries using the rule-based algorithm and use NLP to comprehend intent.
Instead of pitting rule-based against AI chatbots, it is easier to take the best of both to provide an enhanced user experience. The hybrid model is perfect for developing task-based projects and conversational experiences.
Advantages of Conversational AI
The global chatbot market is predicted to grow from $ 190.8 million in 2016 to $ 1.25 billion by 2025. This statistic shows how businesses invest heavily in chatbot technology and the market.
The dramatic adoption of this technology can be attributed to them becoming advanced and intuitive and reducing development and deployment costs.
First, look at this innovative technology’s significant benefits in detail.
Provides personalized conversations across multiple channels
Today’s empowered customers expect glitch-free customer service from organizations regardless of their size and capabilities. Conversational AI helps these organizations provide top-class customer service through personalized conversations across multiple channels.
Customers can enjoy a seamless personal journey even when they move from a social media conversation to a live web chat.
Seamlessly Scale to Meet High Call Volumes
A sudden increase in call volume is expected, and a Conversational AI can help customer service teams handle such spikes. A conversational AI can segregate interactions based on the customer’s intent, requirement, past call history, sentiments, and emotions. A chatbot can help categorize low-value calls from high-value calls, route the low-value ones to Virtual Assistants and ensure live agents handle the more critical calls.
Chatbots can help businesses reduce customer service inquiries’ interaction and response time. By dramatically cutting the time spent on support calls, it is forecasted that by 2023 businesses can save more than $2.5 billion hours in the retail, banking, and healthcare sectors.
Bring Customer Services a Notch Higher
Customer experience has become one of the biggest differentiators in brands. So, it’s not a wonder why brands are jostling against each other to deliver a memorable experience to users. Conversational AI is helping brands deliver a positive experience.
In addition to personalized conversations, customers also enjoy instant, credible responses to their queries at all times. Businesses can develop customer-centric responses to user queries using speech recognition technology. Chatbots can assist by analyzing sentiment, emotion, and intent, reducing live-agent assistance, and increasing first contact resolution.
Aid in Marketing and Sales
Marketing a brand to an audience is a challenging task. Still, businesses are using Conversational AI to create a unique identity for brands and develop a competitive advantage over the market. Businesses are also delivering targeted marketing and conversion techniques.
When you bring an AI-based chatbot to the marketing mix, you can develop an extensive buyer profile, access their buying preferences, and design personalized content tailored to their needs.
Automate Customer Care (Cost Saving)
Another benefit of using chatbots is cost-efficiency. By 2022, it was predicted that chatbots could help businesses reduce their costs by $8 billion per year. Businesses can develop chatbots to handle more straightforward and complex queries instead of continuously training groups of customer service agents to meet customers’ changing needs. Although the initial implementation costs could be high, the benefits outweigh any implementation hiccups.
Mitigate Common Data Challenges in Conversational AI
Conversational AI is dynamically transforming human-computer communication. And many businesses are keen on developing advanced conversational AI tools and applications that can alter how business is done. However, before developing a chatbot that can facilitate better communication between you and your customers, you must look at the many developmental pitfalls you might face.
Developing a chat assistant that can cater to several languages is challenging. In addition, the sheer diversity of global languages makes it a challenge to develop a chatbot that seamlessly provides customer service to all customers.
In 2022, about 1.5 billion people spoke English worldwide, followed by Chinese Mandarin with 1.1 billion speakers. Although English is the most spoken and studied foreign language globally, only about 20% of the world population speaks it. It makes the rest of the global population – 80% – speak languages other than English. So, when developing a chatbot, you must also consider language diversity.
Human beings speak different languages and the same language differently. Unfortunately, it is still impossible for a machine to fully comprehend spoken language variability, factoring in the emotions, dialects, pronunciation, accents, and nuances.
Our words and language choice are also reflected in how we type. A machine can be expected to understand and appreciate the variability of language only when a group of annotators trains it on various speech datasets.
Dynamism in Speech
Another major challenge in developing a conversational AI is bringing speech dynamism into the fray. For example, we use several fillers, pauses, sentence fragments, and undecipherable sounds when talking. In addition, speech is much more complex than the written word since we don’t usually pause between every word and stress on the right syllable.
When we listen to others, we tend to derive the intent and meaning of their conversation using our lifetime of experiences. As a result, we contextualize and comprehend their words even when it is ambiguous. However, a machine is incapable of this quality.
Noisy data or background noise is data that doesn’t provide value to the conversations, such as doorbells, dogs, kids, and other background sounds. Therefore, it is essential to scrub or filter the audio files of these sounds and train the AI system to identify the sounds that matter and those that don’t.
Pros & Cons of different Speech Data Types
Building an AI-powered voice recognition system or a conversational AI requires tons of training and testing datasets. However, having access to such quality datasets – reliable and meeting your specific project needs – is not easy. Yet, there are options available for businesses looking for training datasets, and each option has advantages and disadvantages.
In case you are looking for a generic dataset type, you have plenty of public speech options available. However, for something more specific and relevant to your project requirement, you might have to collect and customize it on your own.
Proprietary Speech Data
The first place to look would be your company’s proprietary data. However, since you have the legal right and consent to use your customer speech data, you could be able to use this massive dataset for training and testing your projects.
- No additional training data collection costs
- The training data is likely relevant to your business
- Speech data also has natural environmental background acoustics, dynamic users, and devices.
- Using such data might cost you a ton of money on permission to record and use.
- The speech data could have language, demographic, or customer base limitations
- Data might be free, but you’ll still pay for the processing, transcription, tagging, and more.
Public speech datasets are another option if you don’t intend to use yours. These datasets are a part of the public domain and could be gathered for open-source projects.
- Public datasets are free and ideal for low-budget projects
- They are available for immediate download
- Public datasets come in a variety of scripted and unscripted sample sets.
- The processing and quality assurance costs could be high
- The quality of public speech datasets vary to a significant degree
- The speech samples offered are usually generic, making them unsuitable for developing specific speech projects
- The datasets are typically biased towards the English language
Explore pre-packaged datasets is another option if public data or proprietary speech data collection doesn’t suit your needs.
The vendor has collected pre-packaged speech datasets for the specific purpose of reselling to clients. This type of dataset could be used to develop generic applications or specific purposes.
- You might get access to a dataset that suits your specific speech data need
- It is more affordable to use a pre-packaged dataset than to collect your own
- You might be able to get access to the dataset quickly
- Since the dataset is pre-packaged, it is not customized to your project needs.
- Moreover, the dataset is not unique to your company as any other business can purchase it.
Choose Custom Collected Datasets
When building a speech application, you would require a training dataset that meets all your specific requirements. However, it is highly unlikely that you get access to a pre-packaged dataset that caters to the unique requirements of your project. The only option available would be to create your dataset or procure the dataset through third-party solution providers.
The datasets for your training and testing needs are completely customizable. You can include language dynamism, speech data variety, and access to various participants. In addition, the dataset can be scaled to meet your project demands on time.
- Datasets are collected for your specific use case. The chance of AI algorithms deviating from the intended outcomes is minimized.
- Control and reduce bias in AI Data
- The datasets can be costly & time-consuming; however, the benefits always outweigh the costs.
Conversational AI Use Cases
The world of possibilities for speech data recognition and voice applications is immense, and they are being used in several industries for a plethora of applications.
Smart Home Appliances/devices
In the Voice Consumer Index 2021, it was reported that close to 66% of users from the US, UK, and Germany interacted with smart speakers, and 31% used some form of voice tech every day. In addition, smart devices such as televisions, lights, security systems, and others respond to voice commands thanks to voice recognition technology.
Voice Search Application
Voice search is one of the most common applications of conversational AI development. About 20% of all searches conducted on Google come from its voice assistant technology. 74% of respondents to a survey said that they used voice search in the last month.
Consumers increasingly rely on voice search for their shopping, customer support, locating businesses or addresses, and conducting inquiries.
Customer support is one of the most prominent use cases of speech recognition technology as it helps improve the customer shopping experience affordably and effectively.
Latest developments in conversational AI products are seeing a significant benefit for healthcare. It is being used extensively by doctors and other medical professionals to capture voice notes, improve diagnosis, provide consultation and maintain patient-doctor communication.
Voice recognition is seeing another use case in the form of security applications where the software determines the unique voice characteristics of individuals. It allows entry or access to applications or premises based on the voice match. Voice biometrics eliminates identity theft, credential duplication, and data misuse.
Vehicular Voice Commands
Vehicles, mostly cars, have voice recognition software that responds to voice commands that enhance vehicular safety. These conversational AI tools accept simple commands such as adjusting the volume, making calls, and selecting radio stations.
The efficiency and accuracy of a voice-enabled car dashboard depend on how it has been trained to hear the user’s voice in as many noisy environments as possible. The voice system in the car dashboard should be able to ascertain the driver’s voice accurately and respond to instructions through unfamiliar background noises such as traffic sounds, rain, thunder, other passenger voices and more.
Home Smart Speaker
Voice assistants should be extensively trained on several voice datasets to identify the speaker and comprehend the instructions by discerning the speaker's voice from background noises such as the kitchen blender, children playing, faint traffic or a lawn mower. It is important to train the model on datasets that have simulated such acoustic environments for better performance.
The model should also be able to determine word fillers or pauses and other sounds such as coughing to determine actual words. Finally, it is crucial to pair the language model with the acoustic model so that the system can convert the words and sounds into meaningful sentences.
Industries Using Conversational AI
Currently, conversational AI is predominantly being used as Chatbots. However, several industries are implementing this technology to garner huge benefits. Some of the industries using conversational AI are:
Conversational AI is having a huge impact on the healthcare sector. Conversational AI has proven to be beneficial for patients, doctors, staff, nurses, and other medical personnel.
Some of the benefits are
- Patient engagement in the post-treatment phase
- Appointment scheduling chatbots
- Answering frequently asked questions and general inquiries
- Symptom assessment
- Identify critical care patients
- Escalation of emergency cases
Conversational AI is helping e-commerce businesses engage with their customers, provide customized recommendations, and sell products.
The eCommerce industry is leveraging the benefits of this best-in-class technology to the hilt.
- Gathering customer information
- Provide relevant product information and recommendations
- Improving customer satisfaction
- Helping place orders and returns
- Answer FAQs
- Cross-sell and upsell products
The banking sector is deploying conversational AI tools to enhance customer interactions, process requests in real-time, and provide a simplified and unified customer experience across multiple channels.
- Allow customers to check their balances in real-time
- Help with deposits
- Assist in filing taxes and applying for loans
- Streamline the banking process by sending bill reminders, notifications, and alerts
Similar to the banking sector, the insurance industry is also being digitally driven by conversational AI and reaping its benefits. For example, conversational AI is helping the insurance industry provide faster and more reliable means of resolving conflicts and claims.
- Provide policy recommendations
- Faster claim settlements
- Eliminate wait times
- Gather feedback and reviews from customers
- Create customer awareness about policies
- Manage faster claims and renewal
When it comes to providing quality and reliable datasets for developing advanced human-machine interaction speech applications, Shaip has been leading the market with its successful deployments. However, with an acute shortage of chatbots and speech assistants, companies are increasingly seeking the services of Shaip – the market leader – to provide customized, accurate, and quality datasets for training and testing for AI projects.
At Shaip, we offer you a broad set of diversified audio dataset for Natural Language Processing (NLP) that mimic conversations with real people to bring your Artificial Intelligence (AI) to life. With our deep understanding of the Multilingual Conversational AI platform, we help you build AI-enabled speech models, with utmost precision with structured datasets in multiple languages from across the globe. We offer multi-lingual audio collection, audio transcription, and audio annotation services based on your requirement, while fully customizing desired intent, utterances, and demographic distribution.
By combining natural language processing, we can provide personalized experiences by helping develop accurate speech applications that mimic human conversations effectively. We use a slew of high-end technologies to deliver high-quality customer experiences. NLP teaches machines to interpret human languages and interact with humans.
Shaip is a leading audio transcription service provider offering a variety of speech/audio files for all types of projects. In addition, Shaip offers a 100% human-generated transcription service to convert Audio and Video files – Interviews, Seminars, Lectures, Podcasts, etc. into easily readable text.
Shaip offers extensive speech labeling services by expertly separating the sounds and speech in an audio file and labeling each file. By accurately separating similar audio sounds and annotating them,
Shaip’s expertise extends to offering excellent speaker diarization solutions by segmenting the audio recording based on their source. Furthermore, the speaker boundaries are accurately identified and classified, such as speaker 1, speaker 2, music, background noise, vehicular sounds, silence, and more, to determine the number of speakers.
Annotation begins with classifying audio files into predetermined categories. The categories depend primarily on the project’s requirements, and they typically include user intent, language, semantic segmentation, background noise, the total number of speakers, and more.
Natural Language Utterance Collection/ Wake-up Words
It is difficult to predict that the client will always choose similar words when asking a question or initiating a request. E.g., “Where is the closest Restaurant?” “Find Restaurants near me” or “Is there a restaurant nearby?”
All three utterances have the same intent but are phrased differently. Through permutation and combination, the expert conversational ai specialists at Shaip will identify all the possible combinations possible to articulate the same request. Shaip collects and annotates utterances and wake-up words, focusing on semantics, context, tone, diction, timing, stress, and dialects.
Multilingual Audio Data Services
Multilingual audio data services are another highly preferred offering from Shaip, as we have a team of data collectors collecting audio data in over 150 languages and dialects across the globe.
Human interactions and communications are often more complicated than we give them credit for. And this innate complication makes it tough to train an ML model to understand human speech accurately.
Moreover, different people from the same demographic or different demographic groups can express the same intent or sentiment differently. So, the speech recognition system must be trained to recognize common intent regardless of the demographic.
To ensure you can train and develop a top-notch ML model, our speech therapists provide extensive and diverse datasets to help the system identify the several ways human beings express the same intent.
Similar to identifying the same intent from different people, your chatbots should also be trained to categorize customer comments into various categories – pre-determined by you. Every chatbot or virtual assistant is designed and developed with a specific purpose. Shaip can classify user intent into predefined categories as required.
Automatic Speech Recognition or ASR
Speech Recognition” refers to converting spoken words into the text; however, voice recognition & speaker identification aims to identify both spoken content and the speaker’s identity. ASR’s accuracy is determined by different parameters, i.e., speaker volume, background noise, recording equipment, etc.
Another interesting facet of human interaction is tone – we intrinsically recognize the meaning of words depending on the tone with which they are uttered. While what we say is important, how we say those words also convey meaning.
For example, a simple phrase such as ‘What Joy!’ could be an exclamation of happiness and could also be intended to be sarcastic. It depends on the tone and stress.
‘What are YOU doing?’
‘WHAT are you doing?’
Both these sentences have the exact words, but the stress on the words is different, changing the entire meaning of the sentences. The chatbot is trained to identify happiness, sarcasm, anger, irritation, and more expressions. It is where the expertise of Shaip’s speech-language pathologists and annotators comes into play.
Audio / Speech Data Collection
When there is a shortage of quality speech datasets, the resulting speech solution can be riddled with issues and lack reliability. Shaip is one of the few providers that deliver multi-lingual audio collections, audio transcription, and annotation tools and services that are fully customizable for the project.
Speech data can be viewed as a spectrum, going from natural speech on one end to unnatural speech on the other. In natural speech, you have the speaker talking in a spontaneous conversational manner. On the other hand, unnatural speech sounds restricted as the speaker is reading off a script. Finally, speakers are prompted to utter words or phrases in a controlled manner in the middle of the spectrum.
Shaip’s expertise extends to providing different types of speech datasets in over 150 languages
Automated Speech Recognition (ASR)
The speakers are asked to utter specific words or phrases from a script in a scripted speech data format. This controlled data format typically includes voice commands where the speaker reads from a pre-prepared script.
At Shaip, we provide a scripted dataset to develop tools for many pronunciations and tonality. Good speech data should include samples from many speakers of different accent groups.
As in real-world scenarios, spontaneous or conversational data is the most natural form of speech. The data could be samples of telephonic conversations or interviews.
Shaip provides a spontaneous speech format to develop chatbots or virtual assistants that need to understand contextual conversations. Therefore, the dataset is crucial for developing advanced and realistic AI-based chatbots.
The utterances speech dataset provided by Shaip is one of the most sought-after in the market. It is because utterances / wake-words trigger voice assistants and prompt them to respond to human queries intelligently.
Our multi-language proficiency helps us offer transcreation datasets with extensive voice samples translating a phrase from one language to another while strictly maintaining the tonality, context, intent, and style.
Text-to-Speech (TTS) Data
We provide highly accurate speech samples that help create authentic and multilingual Text-to-Speech products. In addition, we provide audio files with their accurately annotated background-noise-free transcripts.
Shaip offers exclusive speech-to-text services by converting recorded speech into reliable text. Since it is a part of the NLP technology and crucial to developing advanced speech assistants, the focus is on words, sentences, pronunciation, and dialects.
Customizing Speech Data Collection
Speech datasets play a crucial role in developing and deploying advanced conversational AI models. However, regardless of the purpose of developing speech solutions, the final product’s accuracy, efficiency, and quality depend on the type and quality of its trained data.
Some organizations have a clear-cut idea about the type of data they require. However, most aren’t fully aware of their project needs and requirements. Therefore, we must provide them with a concrete idea about the audio data collection methodologies used by Shaip.
Target languages and demographics can be determined based on the project. In addition, speech data can be customized based on the demography, such as age, educational qualification, etc. Countries are another customizing factor in sampling data collection as they can influence the project’s outcome.
With the language and dialect needed in mind, audio samples for the specified language are collected and customized based on the proficiency required – native or non-native level speakers.
The size of the audio sample plays a critical role in determining the project’s performance. Therefore, the total number of respondents should be considered for data collection. The total number of utterances or speech repetitions per participant or total participants should also be considered.
The script is one of the most crucial elements in a data collection strategy. Therefore, it is essential to determine the data script needed for the project – scripted, unscripted, utterances, or wake words.
Audio of the speech data plays a vital role in developing voice and sound recognition solutions. The audio quality and background noise can impact the outcome of model training.
Speech data collection should ensure file format, compression, content structure, and pre-processing requirements can be customized to meet project demands.
Delivery of Audio Files
A highly critical component of speech data collection is the delivery of audio files as per client requirements. As a result, data segmentation, transcription, and labeling services provided by Shaip are some of the most sought-after by businesses for their benchmarked quality and scalability.
Moreover, we also follow file-naming conventions for immediate use and strictly adhere to the delivery timelines for quick deployment.
Audio / Speech Data Licensing
Shaip offers unmatched off-the-shelf quality speech datasets that can be customized to suit your project’s specific needs. Most of our datasets can fit into every budget, and the data is scalable to meet all future project demands. We offer 40k+ hours of off-the-shelf speech datasets in 100+ dialects in over 50 languages. We also provide a range of audio types, including spontaneous, monologue, scripted, and wake-up words. View the entire Data Catalog.
We have worked with some of the top businesses and brands and have provided them with conversational AI solutions of the highest order.
Some of our success stories include,
- We had developed a speech recognition dataset with more than 10,000 hours of multi-language transcriptions, conversations, and audio files to train and build a live chatbot.
- We built a high-quality dataset of 1000s of conversations of 6 turns per conversation used for insurance chatbot training.
- Our team of 3000 plus linguistic experts provided more than 1000 hours of audio files and transcripts in 27 native languages for training and testing a digital assistant.
- Our team of annotators and linguistic experts also collected and delivered 20,000 and more hours of utterances in more than 27 global languages quickly.
- Our Automatic Speech Recognition services are one of the most preferred by the industry. We provided reliably labeled audio files, ensuring specific attention to pronunciation, tone, and intent using a wide range of transcriptions and lexicon from diverse speaker sets to improve the reliability of ASR models.
Our success stories stem from the commitment of our team to always provide the best services using the latest technologies to our clients. What makes us different is that our work is backed by expert annotators who provide unbiased and accurate datasets of gold-standard annotations.
Our data collection team of over 30,000 contributors can source, scale, and deliver high-quality datasets that aid in the quick deployment of ML models. In addition, we work on the latest AI-based platform and have the ability to provide accelerated speech data solutions to businesses much faster than our nearest competitors.
We honestly believe this guide was resourceful to you and that you have most of your questions answered. However, if you’re still not convinced about a reliable vendor, look no further.
We, at Shaip, are a premier data annotation company. We have experts in the field who understand data and its allied concerns like no other. We could be your ideal partners as we bring to table competencies like commitment, confidentiality, flexibility and ownership to each project or collaboration.
So, regardless of the type of data you intend to get annotations for, you could find that veteran team in us to meet your demands and goals. Get your AI models optimized for learning with us.