Toronto-based Messagepoint operates a documentation and workflow management platform in the client communications industry. 

Founded in 1998, the company’s clients include Xerox and TD Bank, with many other customers in highly regulated fields, such as US health insurance. This particular specialization prodded Messagepoint to develop its Healthcare Touchpoint Exchange tool, part of a larger AI translation system.

The recently integrated tool was recognized by the Business Intelligence Group with a 2025 BIG Innovation Award. The launch aligns with the rapidly accelerating trend of Translation as a Feature (TaaF), where B2B SaaS products incorporate large language models (LLMs) making AI translation much more accessible.

Messagepoint’s AI translation likewise builds on a proprietary AI engine, Messagepoint’s Automation, Rationalization & Content Intelligence Engine (MARCIE), plus third-party technologies from OpenAI and DeepL.

In addition to translation and QA, Messagepoint’s tool can also automate checks for semantic similarity, formatting, structure, variables, and named entities across multiple language versions.

“Preservation of complex formatting and data-driven variable content is very important” — Steve Biancaniello, CEO, Messagepoint

But beyond language quality, measuring the impact of AI translation on speed, workloads, efficiency, and cost is essential to winning over potential clients.

Insurers required to translate complex Medicare Advantage plan materials, for instance, might need to translate documents several hundred pages long within tight timeframes, with insurers in some regions supporting up to 24 languages. Customers might spend “six to seven figures annually” on such work Messagepoint CEO Steve Biancaniello told Slator.

He explained, “Measuring the ROI of AI translation is straightforward because many of our customers previously relied on traditional, human-led translation services, where costs and hours were closely tracked.”

Speed, Scale, and Savings

Of course, beyond the AI translation system’s capabilities for translation and translation accuracy checks, its speed and scale — which Biancaniello described as “far beyond human capability” — are major draws. 

The exact numbers related to time and cost savings “vary based on how much AI is leveraged versus human activity,” Biancaniello said. Clients self-report their human inputs while Messagepoint tracks its own team’s time spent facilitating the translation process. 

“Measuring the ROI of AI translation is straightforward because many of our customers previously relied on traditional, human-led translation services, where costs and hours were closely tracked.” — Steve Biancaniello, CEO, Messagepoint

The figures may include a slightly more elusive measure; namely, time (and money) saved by not reworking translations after the fact. 

“Preservation of complex formatting and data-driven variable content is very important so customers don’t have to reimplement translated content into the content hub,” Biancaniello explained.

To that end, he noted, Messagepoint tries to tackle “the process surrounding translation” rather than “delivering something that automates just one part of a business process.”

Biancaniello said that the company has already extended its AI translation offering to customers in financial services and government, which have historically translated “the bare minimum of customer communications because of the cost and effort required.” 

But serving increasingly diverse populations, especially those with limited English proficiency (LEP), combined with regulatory pressures, has driven demand for translated content to an all-time high, Biancaniello said. 

He reported that the client response to Messagepoint’s AI translation has been “overwhelmingly positive, particularly in the health payer and government sectors.”

Biancaniello predicts the impact of AI translation will only grow as organizations move to embrace AI for translation more broadly, and as the public and clients build trust and confidence in AI.