Up to this point, using AI for translation has often meant hopping across three, four, or even five AI tools just to feel reasonably safe. One model might start to “hallucinate,” another may flatten crucial nuance, and the person ordering the translation usually doesn’t know the target language well enough to diagnose what went wrong. This reality is especially familiar for small and mid-sized enterprises (SMEs), agencies, and subject-matter professionals who aren’t linguists and who simply can’t justify full human post-editing on every email, memo, or web page.
SMART on MachineTranslation.com eliminates that constant uncertainty. With a single action, it surfaces the sentence-level translation that most participating AI engines converge on (so teams are no longer forced to guess which individual AI output deserves their trust).
The model: many engines, one consensus translation
SMART checks several independent AI systems and automatically picks the translation that the majority of engines support for each sentence (with no extra paraphrasing layer, rewriting, or stylistic “polish” applied on top ). The end result is one production-ready version that can be dropped straight into a contract, landing page, CMS, ticketing system, or internal chat.
“When you see independent AI systems lining up behind the same segments, you get one outcome that’s genuinely dependable ,” said Rachelle Garcia, AI Lead at Tomedes. “It turns the old routine of ‘compare every candidate output manually’ into simply ‘scan what actually matters.’”
Why this is particularly relevant now
- Fewer serious misfires: Across internal evaluations on mixed business and legal material, consensus-driven choices reduced visible AI errors and stylistic drift by roughly 18–22% compared with relying on a single engine. The largest gains came from fewer hallucinated facts, tighter terminology, and fewer dropped words.
- More assurance for non-linguists: In a focused review where professional linguists rated SMART output, 9 out of 10 described it as the safest entry point for stakeholders who don’t speak the target language at all.
- A meaningful upgrade from “ship and hope”: When multiple engines converge on the same sentence, the odds of fabricated or invented content drop sharply, helping teams move from draft to delivery much faster.
“MachineTranslation.com is no longer just a scoring and benchmarking layer for AI outputs; it now builds a single, trustworthy translation from those outputs, end to end, ” said Ofer Tirosh, CEO of Tomedes. “We’ve evolved beyond pure comparison into active composition, and SMART surfaces the most robust translation – not merely the highest-ranked candidate. ”
Key pain points SMART is designed to address
- Hallucinations: If one engine fabricates a detail, the others typically don’t – SMART follows the majority rather than the outlier.
- Unfamiliar target languages: Non-speakers finally see “the version that most AIs align on,” instead of having to trust a single opaque suggestion.
- Review bottlenecks: Editors and reviewers don’t have to sift through five near-identical variants of the same sentence anymore.
- SME resource limits: Lean teams rarely have bandwidth for exhaustive linguistic QA on every piece of content – SMART gives them a safer baseline by default.