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Which of 2016’s CAT Offerings Will Disrupt the Market in 2017?
December 14, 2016
in the following blogs:
Translation and Localization
In 2016 we have seen more than the usual number of new offerings in language technology that claim to be something new or disruptive. CSOFT has bet on chat-based mobile translation with its
, which strips away most of the traditional CAT paradigm. Google, Microsoft, and SYSTRAN have made
major announcements around neural MT
and captured mainstream press attention, with Google propagating more than a little unrealistic hype. Lilt and SDL
want to change how translators interact with MT
. And now we’re also seen new interest in machine interpretation technologies, driven by a few viral marketing videos for a product that
may or may not ever appear
Although it has been around for a little while, we see a new contender for the role of market disruptor in SmartCAT, a spin-off from Russian language services power-house ABBYY. We recently sat down with Ivan Smolnikov, CEO of the company, to learn more about how it is trying to disrupt the CAT tool market. It has a business model that represents a direct challenge to how computer-aided translation (CAT) tools are distributed and perceived.
Since the 1990s, CAT tools have had two primary distribution models – for the desktop and as a cloud-based application. In both models – with the exception of
– developers attempt to monetize the software directly by sale or subscription. Some sell licenses by the seat or the enterprise to both LSPs and translators, while others provide free versions that allow translators to work on projects and paid versions to LSPs that let them create and manage projects. In the case of cloud-based systems such as
, part of what buyers pay for is access to the developers’ cloud-based infrastructure, which does away with the need to send files back and forth.
SmartCAT starts with the cloud-based model but adds a significant twist: It is giving away access to the cloud platform. Unlike a typical
that restricts free usage to a small volume or a limited feature set, the company puts no volume caps on its platform and offers the full system to all users. It includes access to a network of freelance translators that use the platform, but SmartCAT does not charge access or brokerage fees to work with them. LSPs or buyers that want to use the platform are free to contact translators, set up jobs, and use the platform as much as they want without giving SmartCAT a cent. This model is similar to that of
, but the latter ties certain advanced features into using shared memories that contribute data back to the platform, whereas SmartCAT does not require that users share any data for full access.
So how does the company, which received
US$2.8 million in venture capital funding
in August, make money? Smolnikov is betting on the power of convenience. SmartCAT has an
payment facility. Users are under no obligation to use it. However, if they do choose to process payments through it, the company takes a cut on the financial transaction. Smolnikov told us that many companies start out using their own financial methods but end up moving to SmartCAT because it takes the hassle out of managing them and that it is cheaper to pay this cut than to manage it themselves. Although he declined to provide names, he told us that a few LSPs have moved entirely to his platform and dropped their in-house payment methods entirely.
For now, the primary attraction for linguists and LSPs is the free CAT tool set, but SmartCAT has its sights set higher. Smolnikov believes that simplification of other aspects can help his company capture ever larger shares of the market and projects from companies with more sophisticated requirements. Some of the major features that are either out or in progress include the following:
The model SmartCAT has adopted is similar to that of Google Docs. Multiple linguists can work on a file simultaneously and see others’ work in real time. Translators can lock segments they’re working on to prevent overwriting or conflicts, at which point only reviewers can work on completed ones. In this model, all linguists working on a project have full transparency and can communicate with each other to ask questions and resolve inconsistencies.
Smolnikov says the real advantage of the collaborative model is that reviewers and proofreaders can start work simultaneously with the appearance of the first translated segments. This allows them to identify problems early on – sometimes within seconds of when they appear – rather than catching them after the fact. He tells us that the result is a much more efficient approach that delivers better quality and throughput because it allows linguists to catch problems early on and fix them before they take time to address.
Simplifying project management.
The company is using AI technology to allow project management via SMS. For example, a project manager can interact with a
to find out whether any linguists are running behind and then reallocate tasks or add personnel via that chat interface. When the system is complete, all reports and performance ratings will be available via SMS. The company is also working on a chat-based quality dashboard. This approach will allow full project control from wherever a project manager has an Internet connection, not just from a dedicated workstation.
By themselves these are important features, but do not set SmartCAT apart from other technology providers with similar capabilities. However, none of the developers we are aware of provides these features at no cost, so their inclusion in the platform represents a challenge to technology providers that see them as a differentiator that would lead prospects to purchase their software.
Because many potential users might be leery of sending data to a Russian company, we asked Smolnikov about how the company addresses the charged issue of its roots. He told us that in the run-up to its round of VC funding, SmartCAT severed all financial and management connections between itself and ABBYY Language Services, although ABBYY does remain a large client. The company is now based in California and maintains its data in Microsoft’s Azure cloud. He pointed out that Russia is not the only country that faces such issues, as many European companies either cannot or do not want to store their data on U.S.-based servers. SmartCAT uses European servers for clients in that region and plans to add them in Hong Kong and mainland China to address the Asian market.
Will SmartCAT manage to challenge the entrenched players in the CAT tools space and capture significant market share? Smolnikov tells us the company has seen 900% growth in the last year and is now processing over 100 million source words per month. As is typical for start-ups, he declined to share precise revenue figures with us, but these volumes suggest that if the company’s bet on the convenience of its payment system pays off, it has the potential to succeed.
Initially its appeal will be for LSPs and projects that do not need a lot of complexity, but the company is clearly not content to take just the low end and has goals to break down further barriers. Even if it doesn’t take off, it puts the current market leaders on notice that traditional business models are under even more pressure, and we think that a lot of its features will become mainstream within a few years.
Based on the growing interest in SmartCAT and the other new technology developers that have emerged in 2016, 2017 is shaping up to be a year in which stable tech providers may have to watch their heels and stay nimble to avoid being left behind. Both SmartCAT and Lilt, with their free-to-use models, are attractive to suppliers in the industry. As they gain traction, they will force traditional CAT tool developers to respond with new pricing models. We do not know which of this new crop of tools – if any – will go on to become the next killer translation app, but it is clear that this next year is already shaping up to be an exciting one for new technology.
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