Visible language risk
Evaluation makes market-specific strengths and failures explicit.
Admas capability
LLM solutions
Move beyond English-first demos with evidence about how language models behave across markets, scripts, and cultural contexts.
Three ways in
Multilingual AI needs language expertise inside the evaluation and product loop—not after deployment.
Choose a focused engagement below, or bring us a problem that crosses the boundaries.
01
Multilingual model evaluation
Task-grounded benchmarks and human evaluation for quality, safety, cultural fit, and language-specific failure modes.
02Model adaptation & fine-tuning
Data and adaptation strategies that improve model behavior for specific languages, domains, and product tasks.
03Multilingual retrieval & safety
Grounded generation, language-aware retrieval, policy evaluation, and safeguards for multilingual AI products.
Signals to act
This work matters when…
- Average benchmarks hide severe failures in priority languages
- Model tone and safety shift unpredictably across locales
- Low-resource language performance cannot be measured reliably
- Teams lack human feedback they can turn into model decisions
What changes
From language risk to operating capability.
Better model behavior
Adaptation is guided by product tasks, language evidence, and human judgment.
Responsible deployment
Grounding, safety, and release gates reflect how the system behaves in each market.
Start here
Let’s solve the llm solutions constraint.
Share the product, languages, timing, and what is not working. We will shape the right starting engagement.
Talk to a specialist