
Multilingual training data
Prompts, responses and conversations written natively, not machine-translated.
Improve multilingual LLM performance with diverse, accurately labeled datasets, for culturally aware AI that scales globally.
Native-speaker review · illustrative
Your package will arrive tomorrow between 9 and 11 am.
approvedআপনার পার্সেলটি আগামীকাল সকাল ৯টা থেকে ১১টার মধ্যে পৌঁছাবে।
approvedSu paquete llegará mañana entre las 9 y las 11 de la mañana.
approvedسيصل طردك غدًا بين الساعة 9 و11 صباحًا.
approvedआपका पैकेज कल सुबह 9 से 11 बजे के बीच पहुँचेगा।
approvedMultilingual services
We work with native speakers and linguistic experts to source, translate and annotate data, with quality control for cultural accuracy, tone consistency and meaning across languages.

Prompts, responses and conversations written natively, not machine-translated.

Translations checked for meaning, tone and cultural fit by native linguists.

Custom collection pipelines for regional and underserved languages.

Outputs reviewed for local norms, dialects and offensive or inaccurate content.

Model outputs rated per language so you can see where performance drops.
Native review
Every language is written or reviewed by native speakers who check meaning, tone and cultural fit, not just grammar.
Source (English)
Sorry for the wait! Your refund is on its way.
Machine translation (Spanish)
¡Perdón por la espera! Su reembolso está en su camino.
Literal phrasing reads unnaturally
Native review
¡Perdón por la espera! Su reembolso ya está en camino.
Natural, matches brand tone
Use cases
Native conversation data so assistants sound natural in every market.
Review of machine-translated output for meaning, tone and cultural fit.
Collection and annotation for languages with little public data.
Human ratings that show where your model's quality drops by language.
How we work
We scope languages and dialects with you, recruit native speakers for each, calibrate on a pilot set and scale with per-language QA.
We review your data, edge cases and success criteria, then write annotation guidelines with you.
A trained team labels a pilot batch, following the agreed guidelines to the letter.
You review the pilot. We fold your feedback into the guidelines before scaling up.
Every batch passes peer review, expert QA and automated checks before it leaves us.
Labels ship in your format on your schedule, ready to drop into training.
Quality pipeline
Results
“Our fashion AI model required pixel-level segmentation across 72 garment categories, and Intellisane AI handled it flawlessly. They quickly adapted to our complex annotation guidelines and delivered consistent, high-quality labels at scale. Their domain focus, speed, and attention to visual detail were exactly what we needed.”
Related
For global chatbots, voice agents, content localization and inclusive AI products.
FAQ
Something else on your mind? Email sales@intellisane.ai.
Get started
Tell us about your data. We'll come back with a plan and a free pilot batch so you can see the quality first.