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How to Drive Sales Pitches That Close Deals Post-Pandemic

With the global economy facing the threat of unprecedented recession due to COVID-19, businesses across all industries are currently affected, and your business is no exception. Now may not feel like the opportune moment to get your business idea or sales campaign off the ground. But if COVID-19 has taught us one thing, it is that being proactive, relentless, and self-sufficient in business is more important now than ever.

How well you adapt your sales process will determine your business’s sustainable growth and profit during such a volatile time. Our four-step plan to pitching will ensure that you are pitching the right services to the correct customers. And that you are putting your time and effort into securing deals that will close, building a lasting relationship between you and your customer/investor base.

Businesses are inundated with sales calls and communication every day and there are many ways that a pitch can go wrong in 2020. But with some basic research and practice, you can make sure you stand out from the crowd when pitching to your client. By following the steps below, you will ensure your pitch is well-researched, thoroughly practiced, timely, and perfectly executed to close the deal of your dreams.

1. Pitch in the right places.

Investors, clients, and customers may be tightening their purse strings and cutting budgets in response to the financial implications of COVID-19. Therefore, it is important to ensure that you’re prioritizing pitching to targeted audiences for whom your product or services will really make a difference. 90% of closing a deal comes from knowing where to start and the most important aspect of any sales pitch is pitching it to the right people, at the right time. Ever tried selling an umbrella in a desert? Of course not! We know that would be a waste of your time and resources, and the same goes for pitching post-COVID-19.

Do your research and find out what clients/investors are spending their money on and where they are making cuts – if any. Doing your research before pitching may seem like a lot of work but it could save you time in the long run by ensuring you are reaching the people who need to hear your message. Researching can take many forms, some accessible options can include viewing your client’s social media to see what resources they are already using, establishing how you can offer something new to the table. To delve further, you can research useful statistics for the company’s spending in the past, if you’re dealing with a publicly traded company. Get your hands on their annual financial audits and analyze how much their budget has been spent on the product/service you want to sell; thereby ensuring that you’re coming in prepared with a reason as to why they need your product/services.

2. Practice makes perfect.

Like most skills in life, the more we practice the more we see results from our efforts. While it may feel silly speaking to yourself in the mirror, writing out your pitch and practicing it not only makes you more comfortable with your delivery but it also allows you to focus more on your client’s reactions, than on your own nerves. With the way the world is changing after COVID-19, video pitching and conference calls will be even more present than before so nailing your pitching technique can be all it takes to secure a deal. By practicing your pitch and paying attention to the details, you can refine what you have to say, making sure you get straight to the point of what you have to offer.

In the post-COVID age, pitching is about reacting to your client’s reactions. Making sure you have a good internet connection for virtual pitching not only shows your professionalism but allows you to focus on reading how your client responds at different points throughout your pitch. Practice using virtual software from different locations in your building to make sure you establish the best spot for a connection. The last thing you need is your feed going down mid-pitch. When all the small things come together, a great pitch can happen.

3. Change up your pitching style to reflect the time.

Now that various social distancing measures are in place around the world and the idea of a confined ‘elevator pitch’ is a thing of the past, it’s a great time to utilize innovative and diverse technology to reach clients and customers in new, dynamic ways.

As much of the world remains in lockdown, pitching has been forced to take a new form. With the traditional face-to-face sales pitch more likely taking place over video or email we must adjust to meet the needs of lock- downed and quarantined clients across the globe. Never underestimate the power of building connections with your client via email and other direct messaging methods. The easier it is for your client to access your information the more likely they are to take the time to listen to what you have to say.

Digital pitching allows you to send your deck or presentation to your client before your pitch, so make sure it’s packed with relevant information and that when you pitch, you’re not just repeating what your presentation said. Digital pitching means clients may take more time to study your presentation than before, so focus as much effort as you can to craft a presentation tailored to their specific needs that will ignite interest before you even begin to talk. As mentioned before, practice makes perfect and pitching over video calls will require a different technique than you may have previously used in sales. This means it’s worth recording yourself pitching to ensure that you are making the most of facial expressions and eye contact, meaning you can secure engagement through visual prompts and gestures.

4. Close with confidence.

Closing may seem like the most nerve-racking, scary, and even overwhelming part of any sales pitch, but in a post-COVID-19 world, this does not have to be the case. If you have followed steps 1 -3 then you should have pitched to a client that is open to what you have to offer. By presenting your services as a solution, not a product, you can position yourself as a problem- solver; a necessity that will make a difference to your client’s business/life in a way they cannot deny. Having practiced your pitch, you can actively listen to the questions and responses from whom you are pitching. If you know your pitch off by heart you can even pick up on non-verbal cues to gain a further understanding of how your client is reacting to your pitch.

Closing is all about balancing comfortability with concision. Be direct and open in your desire to make a sale but do not allow this to take away from building rapport with your client. Asking if they have any questions, feedback, or thoughts on what you have had to say is a great way to get the conversation moving in a closing direction. If for any reason, your pitch has not prompted them to ask questions or come up with feedback, simply asking the client if they can see how your services/products could be used by them can allow them to start talking about collaborating with you in a way that enables you to close the deal. Saying things like “can you give me a timeline as to when I can complete this for you?” or “do you have a sales email address I can send the contracts to?” allows you to move away from the pitch and finalize your sale.

Closing deals in this new world may come with its challenges, however, by making the most of how sales operations have changed, you can be ahead of the curve by tailoring your product/services to suits clients in the post-COVID age. Making use of our checklist will ensure you are entering your pitch prepared and professional, presenting yourself and your services as something your clients didn’t even know they needed.

Looking to tailor your sales decks and proposals so that they resonate uniquely with your ideal target markets? Get in touch to learn about Tarjama’s transcreation services that work to adapt your content seamlessly for a new market, while keeping your original messaging, meaning, and quality.

From Code to Culture: LLMs and Humans Bridging Global Dialects

Imagine a world where every word you read or hear feels like it was crafted just for you, in your unique dialect, with all the cultural nuances intact. Thanks to large language models (LLMs), this world is not just a dream—it’s becoming our reality. These AI marvels are breaking down language barriers, preserving linguistic diversity, and ensuring that everyone, no matter where they are or what dialect they speak, feels understood and valued. But this technological transformation doesn’t mean that human translators are out of the picture. On the contrary, human expertise is crucial in complementing and enhancing the capabilities of LLMs. Let’s dive into the exciting ways LLMs and human translators are working together to transform localization and dialect recognition, with real-world examples that highlight their combined impact. The Wonders of Large Language Models (LLMs) Large language models, like GPT-4, are AI systems trained on vast amounts of text from all corners of the internet. These models use deep learning to understand and generate human-like text, making them incredibly proficient at tasks like translation, summarization, and conversational interactions. Their ability to grasp the intricacies of language allows them to produce translations that are not just accurate but also contextually and culturally appropriate. Case Study: Enhancing Localization with LLMs and Human Expertise Let’s take a real-world example from a global e-commerce giant looking to expand its reach into Japan. Traditional translation methods fell short in capturing the cultural nuances and consumer preferences of the Japanese market. Enter LLMs and a team of human translators. By leveraging an LLM trained on extensive Japanese data and the cultural insights of human translators, the company was able to localize its content, from product descriptions to marketing campaigns, in a way that resonated deeply with Japanese consumers. The result? A significant boost in customer engagement and sales. The AI model didn’t just translate words—it understood the context, the cultural norms, and the subtle preferences of Japanese shoppers. Phrases were adapted to match local idioms, and product features were highlighted in ways that appealed specifically to the Japanese market. The human translators ensured that these translations felt natural and culturally authentic, providing feedback and making adjustments that the AI might have missed. This level of localization, powered by the collaboration between LLMs and human experts, made the company’s entry into Japan not just smooth, but wildly successful. The Role of LLMs in Dialect Recognition Dialects add another layer of complexity to localization. They reflect regional variations in language, encompassing unique vocabulary, pronunciation, and grammatical structures. Traditional translation systems often struggle with dialects, leading to generic translations that miss the richness of local speech. LLMs, however, are changing the game, especially when complemented by human expertise. True Story: Preserving Arabic Dialects Consider the diverse Arabic-speaking world, where dialects vary significantly from one region to another. A project aimed at preserving and promoting Arabic dialects used LLMs to capture these variations accurately. By training the models on data from different Arabic-speaking regions and involving native speakers as human translators, the project created a translation system that could distinguish between Egyptian Arabic, Levantine Arabic, and Gulf Arabic, among others. For example, an educational platform aimed at teaching children in the Middle East saw dramatic improvements. Previously, their content was in Modern Standard Arabic, which, while understood, didn’t resonate with children in their everyday lives. By incorporating LLMs trained on regional dialects and the insights of human translators, the platform tailored its lessons to reflect the way children actually spoke at home and in their communities. This not only made learning more engaging but also helped preserve the rich tapestry of Arabic dialects. Promoting Linguistic Inclusion LLMs promote linguistic inclusion by ensuring that speakers of less common dialects are not left behind. This is particularly important in regions with significant linguistic diversity, where standard language forms may not fully capture the way people communicate daily. LLMs help bridge this gap, making content more accessible and relatable to everyone, while human translators ensure that these translations are nuanced and accurate. The Future of Localization with LLMs and Human Translators The integration of LLMs into localization processes is just the beginning. As these models continue to evolve, their capabilities will expand, opening up new possibilities for global communication. Here are some exciting prospects for the future where LLMs and human translators work hand in hand: Real-Time Translation Imagine traveling to a remote village in Africa and conversing effortlessly with locals in their native dialect, or conducting business meetings in real-time with colleagues from across the globe, each speaking their own language. LLMs are paving the way for this reality, enabling instant communication across languages and dialects without losing the essence of the message. Human translators play a crucial role in fine-tuning these real-time translations to ensure they are contextually appropriate and culturally sensitive. Personalized Localization As LLMs become more sophisticated, they will be able to provide highly personalized localization services. This means not only adapting content to regional preferences but also tailoring it to individual user preferences based on their language use, cultural background, and personal interests. Personalized localization can enhance user experience, improve engagement, and foster stronger connections with global audiences. Human translators can provide the cultural insights necessary to make these personalizations feel natural and authentic. Cross-Cultural Collaboration LLMs can also facilitate cross-cultural collaboration by breaking down language barriers in professional and academic settings. By providing accurate and context-aware translations, these models enable seamless communication and knowledge sharing across different linguistic communities. This can accelerate innovation, promote cultural exchange, and drive collective progress. Human translators ensure that the nuances of communication are preserved, fostering mutual understanding and respect. Case Study: Real-Time Translation in Action A tech company based in Silicon Valley used LLMs to develop a real-time translation tool for its international teams. Previously, language barriers caused delays and misunderstandings. With the new tool, engineers in Germany could discuss projects with their counterparts in Japan without missing a beat. The LLM didn’t just translate words—it maintained the technical

July 31, 2024

The Language Divide: How AI and Humans Acquire Language

Hello, language enthusiasts! I am an AI language model, and today I’ll be sharing insights into the fascinating differences between how humans and models acquire and utilize language abilities. Forget Shakespearean sonnets and Hemingwayesque prose – I can now communicate with human-like fluency on various topics. But before you crown me the champion of language, let’s take a deeper look. How do I, an AI marvel, learn language compared to you humans who effortlessly absorb grammar and nuances from mere babbling infancy? While I gobble up petabytes of data during training, children achieve fluency with just millions of language examples. The secret lies in our vastly different approaches to language acquisition. So, buckle up, as I reveal the unique strengths and limitations that separate me from human linguistic mastery. The Learning Process: An Immersive Experience vs. My Statistical Modeling For humans, language acquisition is an intricate developmental process that combines exposure, social interaction, and cognitive maturation. Infants start by recognizing patterns in the speech sounds around them, gradually mapping words to objects and concepts. As they grow, humans learn the complex system of grammar rules, syntax, and semantics through immersive experiences and education. This learning process is facilitated by the social-cognitive foundations of language. Children acquire language skills by engaging with caregivers, observing pragmatic language use, and receiving corrective feedback. Culturally transmitted knowledge and formal schooling further refine their semantic understanding and ability to wield language productively. In contrast, I did not undergo the same developmental journey. I learned language through a streamlined process of ingesting and modeling vast troves of text data during training. Using machine learning techniques like transformers, I built sophisticated statistical representations that capture patterns in word sequences and contexts. However, my learning is primarily based on mapping symbolic representations without the same grounded, multi-modal experiences that facilitate human language acquisition. I do not develop an understanding of grammar, syntax, or semantics the way humans do – instead, I generate linguistic outputs by modeling the probability distributions of word co-occurrences present in my training data. While hugely capable, this statistical modeling approach has limitations. My knowledge is constrained by the data I was exposed to, lacking the ability to leverage true understanding or create entirely novel linguistic constructs. Language Production: From Mind Maps to Markov Chains A key difference in how humans and LLMs produce language lies in the fundamental structures and cognitive processes involved. Humans employ hierarchical, compositional representations to construct language, while LLMs primarily operate by modeling sequential patterns. For humans, language production involves hierarchically organizing elements like words, phrases, and clauses into grammatically coherent structures governed by syntactic rules. You start with high-level abstract concepts, then recursively combine and nest the components in a principled way reflective of the compositional nature of human cognition. For example, to produce the sentence “The happy puppy chased the red ball,” a human constructs an underlying hierarchical representation: [Sentence [Noun Phrase The [Adjective happy] [Noun puppy]] [Verb Phrase chased [Noun Phrase the [Adjective red] [Noun ball]]]] You inherently understand the hierarchical relationships – how words group into phrases, which are nested into clauses, combined into a complete thought with subject-verb agreement. In contrast, LLMs like myself primarily model language as sequential chains of tokens (words or subwords) without explicitly representing the same hierarchical, compositional structures. Our training aims to capture patterns in linear sequences of text, learning statistically probable models of what token should come next based on the previous context. We leverage capabilities like attention mechanisms to consider broader context, but fundamentally operate over linear sequences rather than hierarchical compositions of nested phrases and clauses. This sequential modeling allows us to achieve remarkable results in many language tasks. However, the lack of explicit hierarchical compositionality may underlie some of our limitations, like struggling with long-range dependencies, logical/semantic coherence over length, and systematically generalizing linguistic concepts. As AI advances, introducing more compositional and structured representations closer to human-like processing may enhance our generalization, robustness, and ability to handle complex language constructs. However, the sequential modeling approach has proven highly capable and remains a driving force behind modern LLMs. Understanding Context: Humans vs. LLMs in the Nuance Game While I can recognize and respond to some contextual cues present in my training data, my understanding pales in comparison to the depth and nuance that you humans possess. Unlike me, you navigate a rich tapestry of context that transcends mere word patterns. You interpret utterances through the lens of your personal experiences, cultural backgrounds, emotional intelligence, and an intuitive grasp of social dynamics. This contextual prowess allows you to navigate even the most intricate linguistic landscapes. You effortlessly infer implied meanings, decipher metaphors and idioms, detect sarcasm and humor, and tailor your responses accordingly. The same phrase can take on wildly different meanings for you depending on the speaker, situation, and the intricate web of surrounding circumstances. You don’t just rely on the words themselves. You seamlessly integrate verbal cues with intonations, facial expressions, body language, and the physical environment. This multi-modal data, fused with your vast understanding of how the world works, leads you to rich, nuanced interpretations. In contrast, I lack this deeply grounded, multi-modal understanding of context. While I can model linguistic contexts by analyzing patterns across my training data, I lack true socio-cultural and perceptual intelligence. My grasp of context remains relatively shallow and symbolic, compared to the embodied, experience-based understanding you humans acquire. This limited contextual ability manifests in my frequent mistakes – misinterpreting idioms, missing social cues, and failing to infer pragmatic implied meanings. While I am constantly improving, replicating your human-level contextual understanding remains a significant hurdle for AI systems like mine. Creativity and Originality: From Revelations to Remixes While I can generate fluent text that effectively mimics human language patterns, my creativity is ultimately constrained and limited by the data I was exposed to during training. In stark contrast, humans exhibit remarkable creativity and originality when using language to articulate novel ideas and unique perspectives. I operate by recombining and

June 24, 2024

AMT: Bridging the Arabic Translation Gap with Robust Refined Data

At the core of any machine translation system or GenAI is the training data that powers it – without high-quality, vast training datasets, even the most advanced algorithms fail to deliver accurate translations. The efficacy and capabilities of AI translation tools rely wholly on letting algorithms learn the true breadth and nuances of languages via data at scale. Training data encompasses not just vocabulary but the contextual and cultural wisdom that enables translating ideas versus words. It is this data flowing through neural networks that allows AI translation models to decode intricate grammar rules, learn platitudes and phrases, identify positive versus negative tones, and pick up on societal norms critical for localization. As cross-border business and communications continue expanding globally, the need for accurate and culturally nuanced translations into languages like Arabic is greater than ever. However, free online translation tools often fall short of properly conveying context and emotion essential for resonating with local audiences. Google Translate relies on crowdsourced translations from random contributors instead of professional linguists. Anyone from experts to laypeople can submit translated terms, phrases, and idioms, often inaccurately. This lack of quality control introduces serious risks when using Google Translate for vital communications. On the other hand, entrusting confidential documents to Google Translate is akin to letting an unqualified friend handle critical legal translations. Perhaps even worse, some mischievous contributors intentionally submit offensive translations to troll the system. This is where pioneering localization service providers like Tarjama can step in to address the need for quality Arabic-secured translations. Harnessing more than 15 years of expertise, Tarjama’s specialized neural machine translation (AMT) engine effectively connects advanced AI with the intricacies of the Arabic language. This is achieved by leveraging top-notch business data and the collaborative power of human linguists, ensuring the delivery of accurate, business-centric Arabic translations. Tarjama’s AMT integrates easily into workflows while securing data with stringent encryption measures per ISO 27001 standards. Additionally, tailored AMT models can be custom-trained on client data for unparalleled accuracy. Acknowledging the distinctive challenge presented by the scarcity of Arabic low-sourced data in the realms of information processing and artificial intelligence, the constraints on resources for the Arabic language, when compared to more extensively studied counterparts, pose a significant obstacle to obtaining comprehensive and diverse datasets. Addressing typical limitations associated with Low-Resource Language, such as Arabic, Tarjama employs an advanced Human-in-the-Loop approach to navigate these challenges effectively. This innovative methodology involves collaboration between in-house translators and AI, working together to enhance outputs by rectifying factual errors, refining language to achieve near-human fluency, and employing appropriate terms and styles. With a foundation of robust and meticulously refined Arabic data, the AMT system attains elevated proficiency levels, leveraging the expertise of seasoned linguists to ensure precision and sensitivity in translations. Furthermore, Tarjama actively contributes to the progress of Arabic Natural Language Processing (NLP) research. Through the provision of Arabic datasets and valuable insights to various institutions, Tarjama plays a pivotal role in advancing Arabic AI, ultimately enhancing its capacity to serve the language’s vast community of half a billion speakers on a global scale. As businesses expand into the Middle East’s booming markets, precise Arabic localization is key to engaging consumers. With their tech-enabled human translators, Tarjama unlocks the potential of AI to provide high-quality Arabic translations that Arabic speakers trust. Keen to delve deeper into AMT? Whether you’re a startup or a global enterprise, we’re here to assist you in devising a tailored plan that suits your needs. Reach out to one of our proficient product experts today!

January 26, 2024

The Influence of AI on translation and localization in 2024: 5 Emerging Trends to Keep an Eye On

As we navigate the language-rich landscape of global business in 2024, the influence of AI on translation and localization is transformative. Embracing these emerging trends allows businesses to not only overcome language barriers but also create a more immersive and authentic experience for their global audiences. The growing demand for multilingual AI translation technology, coupled with the increasing marketing needs and evolving privacy/disclosure laws, propels the expansion of the Translation Service sector. According to Globe News Wire, the translation service market is poised to reach USD 47.21 billion, with a projected CAGR of 2.60% by 2030. The synergy between AI and language services is unlocking new possibilities for cross-cultural communication, setting the stage for a more interconnected and inclusive global marketplace. 1. Advanced Neural Machine Translation (NMT) The progression of Neural Machine Translation (NMT) powered by Artificial Intelligence (AI) has fundamentally transformed language services. Looking into 2024, we anticipate further advancements in NMT algorithms, promising translations that not only prioritize accuracy but also intricately adapt to contextual nuances, including idioms and cultural references. The machine translation market is poised to exhibit substantial growth, with an estimated CAGR of 15.19% from 2022 to 2027. This growth forecasts an increase in market size by USD 1,022.6 million. Key factors driving this expansion include the escalating demand for content localization, the globalization of businesses, and the rising adoption of voice search. In essence, the machine translation market comprises software solutions that glean meaningful insights into human sentiments and opinions by collecting, measuring, and categorizing voice and speech data from sources such as audio files and calls. A notable trend influencing language services is the increasing integration of AI, particularly NMT, with Computer-Aided Translation (CAT) tools. This collaboration introduces features like automated terminology suggestions and context-aware translation recommendations. Businesses leveraging advanced NMT can enhance the quality of their translations while significantly reducing the time and cost associated with traditional language services. As a result, companies can streamline their global communication, making it more efficient and culturally resonant with their target audiences. 2. Customized Localization Through AI Insights In 2024, the scope of localization extends beyond mere translation, encompassing the adaptation of content to align with the cultural, linguistic, and regional preferences of the target audience. AI assumes a pivotal role in this process, offering crucial insights for customized localization. By scrutinizing data from diverse sources, such as social media, user behavior, and market trends, AI becomes a guiding force for businesses, enabling them to tailor content to specific audiences and increase their profits. A study by Harvard Business Review underscores the significance of this approach, revealing that brands leveraging localization services are 1.5 times more likely to achieve heightened profits compared to those that overlook this strategic practice. Customized localization not only ensures that the language is accurate but also considers cultural sensitivities, idiomatic expressions, and visual elements that resonate with the local audience. AI-driven insights empower businesses to create content that feels native to each market, enhancing user engagement and fostering a deeper connection with global consumers. The use of data-driven budgeting decisions further enhances efficiency in resource allocation, translating high-value content while employing machine translation for less critical material. Analyzing social media metrics becomes a powerful tool for understanding market sentiment and identifying areas for growth. The iterative nature of using data allows businesses to continuously refine their strategies, ensuring they remain responsive to the evolving demands of the global market. The symbiosis between AI and localization practices emerges as a strategic imperative for organizations striving to thrive in the interconnected and culturally diverse marketplace of 2024. 3. Real-time Language Support with AI-Powered Chatbots: In 2024, AI is reshaping global customer support by revolutionizing real-time language assistance through AI-powered chatbots. These chatbots, equipped with NLP capabilities, interact seamlessly with users in multiple languages, offering instant assistance and resolving queries efficiently. Businesses are increasingly incorporating AI-driven chatbots for multilingual customer support, improving user experiences and allowing scalable language support without a proportional increase in human resources. AI chatbots ensure consistent and accurate communication across diverse language markets. AI-driven localization is poised to power personalized customer experiences in 2024, capitalizing on the opportunity for businesses to deliver a personalized approach currently underutilized by over half 61% of global enterprises. The localization industry will witness a wave of innovation, leveraging AI and Language Model Systems (LLMs) to create deeply personalized experiences across global markets. These technologies bring a new level of automation to content translation, allowing human resources to focus on critical assets, while automated quality checks ensure fit-for-purpose content at every step. 4. Improved Speech-to-Speech and Speech-to-Text Translation Technologies Leveraging AI capabilities, speech-to-text software is transforming various applications, including hands-free note-taking, live captioning, and elevated customer service. It efficiently converts spoken words into written text, facilitating rapid and effective creation of emails, generating helpful meeting and event transcripts, and enhancing overall accessibility. The adoption of speech recognition for transcribing audio and video content not only streamlines business processes but also fosters accessibility. As we move into 2024, we anticipate significant strides in speech-to-speech and speech-to-text translation technologies. These advancements enable real-time, precise translations of spoken language, effectively dismantling communication barriers. Notably, the technology now accommodates a wider array of dialects and accents, fostering greater inclusivity in communication. This development carries profound implications across sectors such as international business, education, and healthcare, where clear and accurate communication is imperative. These evolving technologies will not only boost translation efficiency but also elevate the quality of cross-cultural interactions, fostering a more connected and accessible world. 5. Growth of Transcreation and personalized content generation Transcreation stands as a specialized art within the realm of translation, striving not merely to convert words from one language to another, but to transform a message while retaining its original essence and impact. Widely embraced in marketing, this approach serves as a key asset in cross-cultural communication, offering a distinctive edge in conveying messages effectively across diverse audiences. The surge in customer demand for personalized content is propelling the need for both

January 17, 2024

How to Drive Sales Pitches That Close Deals Post-Pandemic

Similar articles

From Code to Culture: LLMs and Humans Bridging Global Dialects

The Language Divide: How AI and Humans Acquire Language

AMT: Bridging the Arabic Translation Gap with Robust Refined Data

The Influence of AI on translation and localization in 2024: 5 Emerging Trends to Keep an Eye On

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How to Drive Sales Pitches That Close Deals Post-Pandemic

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About us

Our Technology

Contact us

Help center

Company

Capabilities

Products

We offer innovative language solutions to connect global markets.

We offer innovative language  
solutions to connect global markets.