Enterprise AI is reaching an inflection point.
As hallucinations and context failures expose the limitations of general-purpose tools, organizations are looking beyond speed and fluency toward systems they can trust in consequential settings. For law firms, financial institutions, and other regulated organizations, that means AI must do more than produce a convincing answer: it must understand specialized terminology, reflect organizational standards, support governance requirements, and improve through continued use.
That shift forms the backdrop to Alexa Translations’ evolution into Apertera. The new brand signals an ambition that extends beyond translation into adaptive, context-aware AI for enterprise communication and decision-making. It also builds on more than two decades of experience serving industries in which precision, consistency, and accountability are essential.
As CEO Gary Kalaci explains, the market’s defining question is no longer simply whether organizations can use AI, but whether they can trust it. Apertera’s answer centres on combining AI with institutional knowledge, professional expertise, ongoing training, and hands-on implementation.
LegalTech.ca spoke with Kalaci about why the company chose this moment to evolve, where generic AI tools fall short in legal environments, how law firms are becoming more discerning buyers, and the role Apertera hopes to play as enterprise AI moves into its next phase.
What changed in the market that made this the right moment for the evolution?
GK: For more than two decades, we’ve worked with organizations in legal, financial, and regulated industries where the cost of getting communication wrong carries real consequences. Accuracy, context, and accountability have always mattered.
What’s changed is the market around us, along with our own global expansion ambitions and the opportunity to apply our technology to industries beyond translation.
Generative AI has shifted the conversation from “Can we use AI?” to “Can we trust AI?” Organizations are realizing that secure systems and fast outputs aren’t enough. In high-stakes environments, AI also needs organizational context, professional expertise, and the ability to adapt over time.
That’s what led to Apertera (formerly Alexa Translations). More than a rebrand, it’s a brand evolution that reflects where the market is heading. We’re building on our foundation in legal, financial, and regulatory industries to help organizations move beyond translation with adaptive, AI-powered enterprise communication that stands up under scrutiny.
Where do generic AI tools fall short for law firms and legal departments?
GK: Generic AI can be powerful, but it wasn’t designed for high-stakes legal work. It can generate fluent, convincing language, yet still miss the context, terminology, or professional judgment that legal work demands.
That’s the difference. In legal environments, it’s not enough for an answer to sound right. It has to reflect your organization’s standards, your preferred language, and the nuances that come from years of experience. A single word can change meaning, increase risk, or create unintended consequences.
Our approach starts by pre-training AI using an organization’s historical knowledge, then continuously improving it with every piece of feedback. Unlike generic AI tools, which effectively start from scratch with each interaction, our AI adapts over time to reflect each organization’s standards and preferences.
Technology alone also isn’t enough. Successful AI adoption depends on thoughtful implementation, change management, and ongoing support. That’s where our professional services division and dedicated client success team make a meaningful difference, working closely with the internal change manager.
We believe the future isn’t AI replacing expertise. It’s AI combined with organizational context and professional expertise to produce work that organizations can trust.
What have legal, financial, and regulated industries taught you about building AI products?
GK: They’ve taught us that trust isn’t built through impressive demos. It’s earned through consistent performance in environments where the stakes are high and every decision can have real consequences.
In these industries, AI has to fit into established workflows, support governance requirements, and deliver results that professionals can stand behind. That means understanding organizational context, adapting over time, and working alongside human expertise rather than replacing it.
Those lessons have shaped how we’ve built our technology from the beginning. Our goal has never been to create AI that simply produces answers. It’s to create AI that organizations can rely on when accuracy, accountability, and consistency matter most. Even the best AI requires dedicated, ongoing training. I often compare it to hiring a talented junior associate: they may have an excellent foundation, but they’ll only produce exceptional work if you invest in training them over time. The same is true of AI. How you continuously train and improve it for an organization makes all the difference. Learning how to effectively interact with it over time can further enhance its impact.
How are law firms evaluating AI vendors differently today?
GK: The conversation has become much more sophisticated. A year or two ago, the focus was often on features and productivity gains. Today, law firms are asking tougher questions about governance, accountability, and how AI fits into their existing workflows.
They’re also looking beyond generic capabilities. They want to know whether a solution understands their organization, supports their standards, and can adapt as their business evolves. In legal environments, trust is built through consistent performance over time.
We’re also seeing far more questions about implementation and customization. Law firms and in-house departments want to understand how we’ll help them adapt AI to their organization’s standards, support implementation, and work alongside them throughout the process. In an era where access to AI is plentiful, the biggest differentiators are customization and effective implementation. The question becomes which tools can best understand your team’s way of working and adapt accordingly. That’s an area where our dedicated client success team has always played an important role.
That’s a positive shift. It reflects a growing understanding that enterprise AI isn’t just about adopting new technology. It’s about choosing technology that’s aligned with how your organization works.
Looking ahead, what role do you see Apertera playing?
GK: We believe the future of enterprise AI isn’t about replacing people. It’s about helping professionals make better, faster, and more informed decisions in complex environments.
That’s the role we see Apertera playing. We’re building AI that understands organizational context, learns over time, and is grounded in more than two decades of legal, financial, and regulatory domain expertise to support high-stakes communication and decision-making.
As AI continues to evolve, our focus is on helping professionals spend less time editing and validating AI output, building validation tools that make it easier for organizations to establish trust in AI-generated work, and, over time, applying those capabilities well beyond the translation industry.