Top 7 Funding in Speech Recognition AI Startups in 2022

(The latter is the subset of the former that is spread to deceive intentionally.) Its platform relies on a large team of expert human reviewers working in tandem with its AI system. Many of Logically’s clients are governments, which use its technology for issues including national security, election integrity and COVID-19 misinformation. Co-founded by AI legend Sebastian Thrun (the creator of Google X and Google’s self-driving car program) and two of his Stanford PhD students, Cresta is the most pedigreed competitor in this category.

According to the company, its technology boosts average revenue per sales rep by 27%, translating into massive ROI for its customers. Founded in 2009, Grammarly has admirably remained abreast of the latest NLP technologies over the years. The company raised funding late last year at a whopping $13 billion valuation. Grammarly’s product provides automated recommendations for improved spelling, grammar, diction and phrasing in real-time as users write. A nascent ecosystem of startups is at the vanguard of this technology revolution. These companies have begun to apply cutting-edge NLP across sectors with a wide range of different product visions and business models.

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MetaDialog’s conversational interface understands any question or request, and responds with a relevant information automatically. AI Engine automatically processes your content into conversational knowledge, it reads everything and understands it on a human level. Healthcare data is incredibly messy, as anyone who has been exposed to the modern healthcare system knows.

Yet by most accounts, the core NLP in Gong’s product offering is not particularly advanced. It therefore is and must be at the heart of our efforts to build artificial intelligence. Your customers are being addressed in real time, AI Engine answers their questions and helps them with anything they need through a chat conversation. There is no one particular NLP “killer app” in healthcare; rather, startups have identified a wide range of different use cases to which language AI can be valuably applied. From misinformation to cyberbullying to hate speech to scams, harmful online content is a massive and growing problem in today’s digital world. Given the size of the market, plenty of smaller startups have emerged with similar AI-driven product offerings.

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These fundraises have made these two startups among today’s first NLP unicorns. Expect VC dollars to continue pouring into this space given the outsize market audio startup gives voice to chatbot opportunity in play. A close Ada competitor, Rasa’s product caters to more technically savvy users, with a greater focus on chatbot configurability.

Other startup competitors in this category include Observe.ai, whose product is oriented around post-conversation analytics rather than real-time coaching, and Level AI, which focuses on automating call center quality assurance. AI Rudder sells to customers in financial services and e-commerce, two industries that make extensive use of call centers. The pandemic has driven rapid growth for AI Rudder, whose revenue quadrupled last year. The company’s AI system can not only speak a wide range of different languages but can also adopt the appropriate regional accent depending on the caller. The leading player in this category is Moveworks, which raised a $200 million Series C from Tiger Global last year. Espressive claims that its chatbot platform can resolve between 50% and 70% of all employee helpdesk tickets without human assistance, recouping over a week of productivity per employee per year.

Search

Duplex is an AI system that, in a remarkably human-sounding voice, can place phone calls on behalf of human users to complete routine tasks like booking a dinner reservation or a hair appointment. The most widely used AI-powered language translation service in the world is Google Translate. Unsurprisingly, given that it is the birthplace of the transformer and the most advanced AI organization in the world, Google has incorporated the latest NLP technologies to vastly upgrade its Translate service in recent years. Textio, LitLingo, and Writer are three newer entrants using next-generation language AI to build advanced Grammarly-like solutions for more targeted use cases. Textio focuses on hiring and recruiting, LitLingo on business compliance and risk management, and Writer on company-wide style and brand consistency.

Next-generation language AI is poised to make the leap from academic research to widespread real-world adoption, generating many billions of dollars of value and transforming entire industries in the years ahead. A handful of young startups have popped up that are nipping at Gong’s heels, though none have yet broken out. Yet certain repeatable principles and tactics do exist that, if systematized, can meaningfully improve a sales team’s performance. The interesting question—for Lilt and for the entire industry—is whether and how quickly the humans in the loop can be phased out in the years ahead. Language barriers are a fundamental impediment to international business and travel, costing untold billions in lost productivity every year. Next-generation NLP promises to transform how humans write, reconceptualizing one of civilization’s most basic and vital activities.

Content Moderation

Conversational AI platforms can automatically field and resolve many of these employee support requests, reducing the need for human intervention and saving organizations vast amounts of time and money in the aggregate. Notwithstanding earlier false starts, chatbots today have begun to gain real market adoption, audio startup gives voice to chatbot thanks to improvements in the underlying NLP as well as in companies’ understanding of how to best productize and deploy these bots. There are few applications of language AI that can more directly affect a company’s top line. Not surprisingly, therefore, the market for sales intelligence AI is booming.

Thus, Lilt offers a hybrid model that combines cutting-edge AI with “humans in the loop” to translate written content for global organizations, from marketing to mobile apps to technical documentation. This partially automated approach enables Lilt to provide translation that is cheaper than using human translators and at the same time more accurate than using AI alone. Machine translation has been a central goal of artificial intelligence researchers dating back to the very beginnings of the field of AI in the 1950s. Automated language translation products have been available since the dawn of the commercial internet in the 1990s.

But significant opportunities also exist for startups in the fast-changing world of language translation. All four of the companies mentioned so far use AI primarily to provide recommendations and insights on existing text that humans have already written. The next frontier in AI-augmented writing will be for the AI to generate novel written content itself based on guidance from the human user. Challenging Google directly will, to state the obvious, be an uphill battle. There is also significant opportunity for startups in search beyond the consumer internet search market with which Google has become synonymous.

Yet as anyone who has experienced writer’s block can attest, writing can be a frustrating experience. The act of translating inchoate thoughts into well-crafted language—of finding the right words—can be time-consuming and unsystematic. In today’s information-based economy, perhaps no skill matters more than effective writing. Primer is an older competitor in this space, founded two years before the invention of the transformer. We have a simple pricing model based on questions asked, refer to our Pricing page to learn more.

• As a result, very few companies or researchers actually build their own NLP models from scratch.
• One final enterprise search startup worth keeping an eye on is Hebbia, which is building an AI research platform to enable companies to extract insights from their private unstructured data.
• A less common approach is to develop contact center AI purpose-built for a particular vertical.

Like You.com, ZIR has a pedigreed founding team that includes former Cloudera CTO/cofounder Amr Awadallah. Building a state-of-the-art NLP model today is incredibly resource-intensive and technically challenging. As a result, very few companies or researchers actually build their own NLP models from scratch. Instead, virtually all advanced NLP in use today, no matter the industry or setting, is based on one of a small handful of massive pretrained language models. Stanford researchers recently dubbed these pretrained models “foundation models” in recognition of their outsize influence.

Founded by Richard Socher, former Chief Scientist at Salesforce and one of the world’s most widely cited NLP researchers, You.com is reconceptualizing the search engine from the ground up. Its product vision includes a horizontal layout, an emphasis on content summarization, and above all, a commitment to user data privacy. Search has been dominated by a single player for so long that it is often seen as an unpromising or even irrelevant category for startups. But there is also tremendous opportunity in this category for younger startups.

The company’s target customers include health insurers, pharmaceutical companies and medical device companies. Last month, contact center AI startup Uniphore raised a $400 million round from NEA that valued the company at $2.5 billion. A few weeks later, direct competitor Cresta announced an $80 million fundraise led by Tiger Global at a $1.6 billion valuation.

Aircover, which raised a seed round last year, and Wingman, which came out of Y Combinator in 2019, are two examples. The most basic way that humans use natural language to interface with machines is through search. It is the primary means by which we access and navigate digital information; it lies at the heart of the modern internet experience.

An aiDriven chatbot contains a simple dashboard and different metrics for estimating results (e.g., chat volume, goal completion rate, fallback rate, or score of satisfaction) which are easy to interpret. Absolutely not, the only thing you need to do is import your data into the system, the rest is done automatically. In just one click connect to all of your content, import data from your website, databases, documents and CRM. MetaDialog can work easily with whatever tools you’re using, including Mailchimp, Zapier, Apify, Amplitude and many, many more.

Following in Duplex’s footsteps, a handful of startups have developed voice AI technology that can engage in nuanced automated phone conversations. While Google’s Duplex is a consumer-facing tool , these startups’ go-to-market efforts focus on the enterprise. And no enterprise opportunity looms larger for this technology than contact centers.