Latest news with #GrantStraker
Techday NZ
07-08-2025
- Business
- Techday NZ
Translation sector shifts to specialist AI models for accuracy
The translation industry is seeing a transition from general-purpose large language models to smaller, specialised models focused on greater accuracy and efficiency. Over the past five years, neural machine translation (NMT) has shifted from a cautious experiment to mainstream adoption, with large language models (LLMs) playing a major role in driving new approaches to content localisation for global businesses. However, the future of the industry appears to be moving towards small language models (SLMs) designed for specific industries and language pairs. Grant Straker, Co-founder and Chief Executive Officer of Straker, has outlined his perspective on the changing landscape: "In just five years, the translation industry has undergone a radical transformation. What began as cautious experimentation with neural machine translation (NMT) has evolved into widespread adoption of large language models (LLMs), reshaping how global businesses approach content localisation. But the next leap forward, I believe, won't come from models that are bigger, broader, and more complex. It will come from something much more focused and far more effective. According to Straker, small language models offer tangible benefits over their larger predecessors. He explained that Straker has observed consistent outperformance by SLMs in their intended roles: "At Straker, we've seen first-hand how Small Language Models (SLMs) - purpose-built for specific industries and language pairs - consistently outperform their larger, general-purpose counterparts. In our experience, SLMs represent the future of translation: more accurate, more efficient, and more commercially viable." The generalist limitation LLMs are undoubtedly powerful, with advanced capabilities to generate human-like text and address diverse tasks. However, Straker noted that this versatility can result in problems in specialised translation, especially in highly regulated industries. He highlighted the example of domain-specific jargon, explaining, "There's no question that LLMs are powerful. Their versatility and capacity to generate human-like text have unlocked incredible new possibilities. But when it comes to translation - especially in regulated sectors - their generalist design becomes a weakness." To illustrate, he pointed to the word "equity," which carries different meanings in real estate and finance. "Without domain-specific training, LLMs can easily misinterpret such terms risking confusion or, worse, critical errors in legal, financial, or medical communications." Operational concerns were also addressed, with Straker stating, "The problem isn't just accuracy. LLMs are resource-hungry, slow to deliver, and often require heavy human editing to meet professional standards. For businesses managing hundreds of content streams across multiple languages, this quickly becomes unsustainable both financially and operationally." The case for smaller models To address these challenges, Straker described the development of Tiri, a suite of SLMs created for translation and localisation tasks. He stated, "To solve this, we developed Tiri - a suite of Small Language Models trained specifically for translation and localisation. These aren't all-purpose bots trying to do everything. Instead, they operate more like expert linguists deeply specialised, highly contextual, and tuned for specific tasks." He shared an example of improved quality and contextual accuracy in financial translation tasks when using a Tiri model: "When we trained a Tiri model to handle Japanese-to-English investor relations material, the gains in quality and contextual accuracy were significant outperforming general-purpose LLMs that lacked financial nuance. That's the power of specialisation." Three business benefits Straker laid out three core reasons why SLMs may better serve the needs of business translation. The first centres on domain accuracy: "Tiri models are trained on high-quality, domain-specific data including translation memories and industry glossaries. They don't just translate words; they understand the context behind them. Whether it's legal contracts, pharmaceutical documentation, or technical manuals, the result is consistent, first-pass accuracy." The second reason relates to efficiency: "Smaller models require less compute power. That means lower costs, faster speeds, and seamless integration into existing workflows without the need for expensive infrastructure or long processing times." The third advantage is continued improvement: "We've embedded Reinforcement Learning from Human Feedback (RLHF) directly into our workflows. This means Tiri models get better over time learning from real-world edits to align more closely with client expectations and preferred tone." Translation as a craft In his remarks, Straker also discussed the philosophical transition in translation with the rise of AI-driven tools: "For us, this isn't just a technical shift, it's a philosophical one. Translation is a craft. It's about preserving meaning, intent, and cultural nuance across borders. AI must honour that, not flatten it." He concluded with his belief in the role of specialised AI models in the future of translation: "That's why I believe the future of translation will be shaped by specialisation, not scale. Businesses don't need AI that knows a little about everything. They need AI that deeply understands their domain, their customers, and their voice. Because in the end, translation isn't just about sounding fluent. It's about being understood."
Mercury
04-06-2025
- Business
- Mercury
Straker adds n8n to growing list of AI platform integrations
Don't miss out on the headlines from Stockhead. Followed categories will be added to My News. Straker has launched an integration with AI workflow platform n8n The company delivered record adjusted EBITDA of $4.8 million in FY25 Ord Minnett has upgraded Straker's price target to 52c Special Report: ASX-listed language tech company Straker has extended its reach into the fast-growing enterprise automation market, announcing a new integration with AI workflow platform n8n. This comes just days after Straker (ASX:STG)reported record profitability and winning a price target upgrade from broker Ord Minnett. The integration introduces Straker's Verify product – its AI-powered translation quality and compliance tool – into n8n's ecosystem, giving over 230,000 active users and 3,000 enterprise clients the ability to automate translations, receive real-time quality scores, and seamlessly escalate content to human linguists when needed. The partnership forms part of Straker's broader platform strategy, which has already seen the company integrate its services into workplace staples like Slack, with plans underway to launch in Microsoft Teams. 'As organisations race to adopt generative AI workflows, translation accuracy and oversight cannot become casualties of speed,' said co-founder and CEO Grant Straker. 'This integration with n8n ensures businesses can move fast without compromising on the quality their global audiences demand. It is yet another example of Straker's strategy of leveraging third-party platforms to cost-effectively broaden the reach of our sales efforts and grow Straker's high-margin recurring revenue.' The new capability is particularly relevant to highly regulated sectors such as financial services, healthcare, and legal, where accuracy and compliance are non-negotiable. Users can deploy Verify to automatically trigger translations, evaluate content quality in real time, and apply human oversight only where needed offering both scale and assurance. Record results, stronger margins The announcement comes off the back of Straker's FY25 financial results, which saw the Auckland-based firm deliver revenue of NZ$44.9 million – at the top end of guidance – and adjusted EBITDA of NZ$4.8 million, a company record. Gross margins rose more than 300 basis points to 67 per cent, reflecting a strategic pivot toward AI-enabled, recurring revenue streams and away from lower-margin, legacy translation work. Ord Minnett responded by lifting its 12-month price target for Straker by 42%to A$0.52 per share, citing stronger-than-expected execution and rising profitability. The broker noted that the company's adjusted EBITDA came in 141% above its forecast and described the platform strategy as a 'margin-accretive opportunity'. 'The earnings uplift and expanding contribution from newer offerings such as SwiftBridge and Verify position the company well heading into FY26,' the note said. The company's SwiftBridge product, developed in partnership with IBM, recently launched in Japan to support new Tokyo Stock Exchange disclosure requirements. Meanwhile, Verify – now accessible via n8n – delivered more than NZ$1 million in new revenue in its first year. Straker ended FY25 with NZ$12.9 million in cash and no debt. The company reduced headcount by 15 per cent across the year, including a 32 per cent cut in its Production segment, contributing to sustained operating leverage as it scales its software-led offerings. While no formal FY26 guidance has been issued, management expects margin expansion and further growth in recurring revenue lines to continue. This article was developed in collaboration with Straker, a Stockhead advertiser at the time of publishing. This article does not constitute financial product advice. You should consider obtaining independent advice before making any financial decisions. Originally published as Language tech specialist Straker adds n8n AI platform integration
Techday NZ
03-06-2025
- Business
- Techday NZ
Straker partners with n8n to automate quality AI translations
Straker has expanded its range of AI-powered translation tools through an integration with n8n, an open-source automation platform used by more than 230,000 active users and over 3,000 enterprise clients worldwide. The newly released Straker Verify node is now available within the n8n environment, introducing key capabilities for organisations aiming to automate content translation workflows. The integration enables users to trigger translations automatically when new content is created, receive real-time quality assessments, and direct specific content to human linguists at Straker for further review if required. Straker's approach targets businesses facing the challenge of maintaining translation quality and compliance as they accelerate their use of AI in content creation and localisation efforts. This is particularly relevant for highly regulated sectors, including finance, healthcare, and legal services, where translation accuracy can have compliance implications. Organisations in these sectors benefit from automation by being able to quickly scale global content delivery while ensuring linguistic accuracy and meeting regulatory obligations. The integration automates the decision-making process by assigning real-time quality scores to translations and routing content that scores below a certain threshold or is considered high priority for expert human review. Grant Straker, Chief Executive Officer and Co-Founder of Straker, said, "As organisations race to adopt generative AI workflows, translation accuracy and oversight cannot become casualties of speed. This integration with n8n ensures businesses can move fast without compromising on the quality their global audiences demand. It is yet another example of Straker's strategy of leveraging third party platforms to cost effectively broaden the reach of our sales efforts and grow Straker's high margin recurring revenue." The integration is consistent with Straker's ongoing strategy to embed its language solutions within widely used workplace platforms. Previously, Straker developed an application for Slack, which allows users to order translations and monitor progress from within the collaboration platform. The company has also announced intentions to offer a similar solution for Microsoft Teams. The n8n automation platform, headquartered in Berlin, is increasingly used as a foundation for building sophisticated AI-enabled business process flows. It offers flexibility through its open-source nature, allowing businesses to deploy the platform on their own infrastructure and maintain control over their data and AI models. According to the announcement, n8n's deep integration with AI models and tools like LangChain enables technical teams to construct workflows that automate research, generation, refinement, and publication of content across channels. Deploying Straker's Verify node within the n8n ecosystem reflects both companies' focus on supporting complex, AI-driven content operations while ensuring workflow flexibility and regulatory compliance. The newly launched integration is accessible to n8n users who register for Verify AI services. This integration with n8n builds on Straker's history of technical development in AI-driven language services, combining automated translation mechanisms with human linguistic expertise where necessary. Both companies highlight the critical importance of balancing speed and accuracy as the adoption of AI in business workflows accelerates globally.
