For companies around Europe which require thousands of words translated per week, the reality can be complex and costly. Thousands of companies are spending over €50,000 per year on translation, with many spending over €100,000. Multiple types of translations and various pricing models (per line, per word, per hour) can make it difficult for translation buyers to evaluate or compare the cost estimates provided by language service providers (LSPs).
Aqrate provides companies and LSPs with a transparent way to do translation pricing, gives companies control of their translations and intellectual assets and allows them to compare and use multiple vendors, while giving LSPs a more standardized, low-risk way of pricing their work, as well as access to a larger client market. Our market tests have shown that our service can deliver up to 30% (or more) savings to translation buyers on their annual translation costs. Aqrate’s unique value proposition goes far beyond mere cost savings, however, to encompasses the entire translation process and deliver value and benefits to all stakeholders: translation providers and buyers.
Our approach is important for society, because we seek to integrate machine translation (MT) and artificial intelligence (AI) tools for repetitive tasks (what software is ideally suited for), while preserving the human element for the linguistic, technical and creative aspects of translation work (what humans are ideally suited for). As AI and MT continue to improve, this approach will ensure that there will always be a place for people in the translation process. In addition, we estimate that we will create 130 high-value jobs in software development, sales, language services and linguistic studies, thus becoming an important employer for trained linguists (i.e. walking the walk, as far as human resources are concerned).
Our objective is to provide Agrate’s software-as-a-service (SaaS) to serve as bridge between translation buyers and LSPs. This will require building up our human linguistic resources, while at the same time improving our software algorithms to perform front- and back-end analysis of texts.
