AI Data & Evaluation
Data collection, annotation and model evaluation that make AI work in hard languages — by native speakers who understand dialect, code-switching and cultural context.
Four service lines
Four lines sold to the same buyers, built on the same speaker base — so adding each one costs little after the first.
Natural speech recordings, handwriting images, dialect conversations and code-switching audio in Hebrew, Arabic and their variants. Structured for ASR, TTS and LLM training.
ASR · TTS · Speech AI · HTRVerbatim transcription, speaker tagging, sentiment labelling and second-pass QA before any delivery. Covering Modern Standard Arabic, dialects, Hebrew and code-switching.
Transcription · MOD · QA · MTPERating model responses in Hebrew and Arabic, RLHF preference pairs, adversarial prompts and cultural safety checks. For AI labs, startups and enterprises launching Arabic or Hebrew products.
LLM · RLHF · Red-teaming · SafetyA human escalation team for cases the bot cannot handle, plus a weekly error report showing where the bot fails. For companies launching Hebrew or Arabic chatbots.
Hybrid CX · Bot QA · EscalationWhy these languages
Arabic splits into dozens of dialects. Hebrew is underrepresented in training data. Users in both regions code-switch constantly with English. Few vendors combine both languages in one team at Egyptian cost.
Hebrew + Arabic + English code-switching in one team — a combination almost no BPO offers.
Government-certified Hebrew translation, BPO operations at Concentrix and Teleperformance, and active AI data projects.
High-quality multilingual talent at a fraction of European or Gulf pricing — without sacrificing language accuracy.
A permanent supply of trained annotators, transcribers and evaluators graduated from our own academy.
Proof of work
We entered AI data before we named it that. These are active assets.
Structured collection of Hebrew handwritten samples with ground-truth transcriptions for OCR and HTR model training.
Native-speaker audio combining Hebrew, Arabic and English in natural conversation — covering dialect variation and code-switching patterns AI models struggle with.
End-to-end transcription pipeline with content moderation and multi-tier QA for Uber's AI team — proving our ability to follow complex annotation guidelines at scale.
How to start
30 minutes to understand your language, volume, format and quality bar.
We propose a small, scoped project — typically 50–200 hours or items — with a fixed price and clear acceptance criteria.
Pilot delivered with full QA report. You assess quality against your own standards before committing to volume.
If the pilot passes, we move to a volume contract with agreed rates, SLAs and a dedicated project lead.