Ongoing evaluation across the languages, markets, and cultural contexts where your AI actually operates.
Into23 helps enterprises replace English-only testing with a practical multilingual evaluation program. We combine native-speaker judgment, culturally grounded prompts, and repeatable reporting so buyers can understand how their AI performs market by market instead of guessing from translated benchmarks.
Starting from $20,000 per program cycle · Pricing reflects language coverage, market count, benchmark design, and reporting cadence.
Assessment is run by reviewers who understand the target language as it is actually used, not just as translated test text.
We help build prompt sets and scoring criteria that reflect local knowledge, customer expectations, and market-specific realities.
Results are broken out by language and locale so teams can see where quality holds and where it drops.
This is designed as an ongoing operating model, not only a one-time benchmark exercise before launch.
We minimise the false signals that appear when English benchmarks are simply translated and reused across markets.
Outputs are shaped for both product teams and business stakeholders who need a clear view of multilingual readiness.
We align the program to the languages, customer journeys, and content types that matter commercially.
Rubrics, prompt sets, benchmark assets, and reviewer guidance are localised for each target market.
Native-speaker evaluators score outputs and flag culturally specific failure modes as the model or workflow evolves.
We deliver market-by-market reporting that supports release decisions, tuning priorities, and governance reviews.
Into23 assembled and managed more than 120 qualified annotators across English, Chinese, Hindi, Japanese, Korean, and Arabic, with calibrated guidelines for red teaming, RLHF, and structured model evaluation.
Translated benchmarks carry English assumptions about phrasing, cultural context, and acceptable responses. Native-speaker evaluation catches market-specific failure modes that translated tests systematically miss.
Any AI system deployed across multiple languages and markets, including chat models, content generation tools, customer service AI, and enterprise assistants where quality must hold across locales.
Yes. Programs are designed to start with priority markets and expand as the AI product grows. We build the framework to scale without rebuilding from scratch.
Get a custom quote for your multilingual AI evaluation project. Our team typically responds within 24 hours.