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Intellisane AI

Multilingual GenAI with native-speaker data

Improve multilingual LLM performance with diverse, accurately labeled datasets, for culturally aware AI that scales globally.

  • Native speakers & linguists
  • Low-resource & regional languages
  • Cultural & safety review
multilingual_eval.jsonlreview
illustrative example
  • MindEarth
  • Taranto
  • Felucca
  • Virtual Staging
  • Algolight
  • Innodata
  • Stay22
  • Toothlens
  • Agnitu
  • Shaip

Multilingual services

Built with native speakers

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.

  • Two people recording a podcast

    Multilingual training data

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

  • Hands typing on a laptop keyboard

    Translation & localization review

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

  • Woman recording speech at a studio microphone

    Low-resource languages

    Custom collection pipelines for regional and underserved languages.

  • Team working together around a table

    Cultural & safety review

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

  • Software developer working on a laptop

    Multilingual evaluation

    Model outputs rated per language so you can see where performance drops.

Native review

Judged by native speakers, not machine translation

Every language is written or reviewed by native speakers who check meaning, tone and cultural fit, not just grammar.

  • Native writers per language
  • Cultural and safety review
  • Per-language quality scores

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

Where teams put this data to work

  • Multilingual chatbots

    Native conversation data so assistants sound natural in every market.

  • Localization QA

    Review of machine-translated output for meaning, tone and cultural fit.

  • Low-resource languages

    Collection and annotation for languages with little public data.

  • Per-language evaluation

    Human ratings that show where your model's quality drops by language.

How we work

From pilot to production

We scope languages and dialects with you, recruit native speakers for each, calibrate on a pilot set and scale with per-language QA.

  1. Step 01

    Consult

    We review your data, edge cases and success criteria, then write annotation guidelines with you.

  2. Step 02

    Annotate

    A trained team labels a pilot batch, following the agreed guidelines to the letter.

  3. Step 03

    Calibrate

    You review the pilot. We fold your feedback into the guidelines before scaling up.

  4. Step 04

    Multi-layer QA

    Every batch passes peer review, expert QA and automated checks before it leaves us.

  5. Step 05

    Deliver

    Labels ship in your format on your schedule, ready to drop into training.

Quality pipeline

Five checkpoints between raw data and your model

98%+
Accuracy guarantee
  1. 01
    Annotator
    Labels against your guidelines
  2. 02
    Peer review
    A second annotator reviews the work
  3. 03
    Expert QA
    Senior reviewer samples and corrects
  4. 04
    Automated checks
    Rules catch missing or invalid labels
  5. 05
    Client sign-off
    You approve before final delivery

Results

Quality you can measure

Accuracy guarantee
98%+
Accuracy guarantee
Returning clients
96%
Returning clients
Cost reduction
2.5x
Cost reduction
Projects completed
200+
Projects completed
MindEarth

“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.”

Valerio Colamatteo
Sr. AI Scientist & Team Lead, MindEarth

Related

For global chatbots, voice agents, content localization and inclusive AI products.

FAQ

Questions, answered

Something else on your mind? Email sales@intellisane.ai.

Get started

Let's scope your project

Tell us about your data. We'll come back with a plan and a free pilot batch so you can see the quality first.