01
Model evaluation
Evaluate Hebrew outputs for accuracy, instruction following, localization, safety, tone, factuality, and recurring failure patterns.
Hebrew-native AI data and evaluation
Make Hebrew AI systems work reliably in production.
Native-language data, model evaluation, and quality assurance for teams building LLM, document AI, speech, NLP, and enterprise automation systems in Hebrew.
Controlled pilots, recurring production batches, and inspectable QA evidence.
Native Hebrew review
Explicit acceptance criteria
Inspectable deliverables
International delivery
Why specialist review matters
Hebrew is not just another localization layer.
Production systems must handle morphology, right-to-left text, mixed Hebrew-English content, domain terminology, real document layouts, speech variation, and inconsistent source data. Generic multilingual workflows can miss defects that are obvious to a native reviewer.
- 01Language judgment is evaluated in context, not only by automated metrics.
- 02Defects are classified by severity and linked to explicit acceptance rules.
- 03Outputs are delivered with evidence that engineering, product, and data teams can inspect.
Capabilities
Focused support across the Hebrew AI quality stack.
Three engagement families cover model behavior, dataset quality, and the language-specific edge cases that surface in document and speech systems.
02
Data quality and annotation
Build, label, audit, redact, and validate Hebrew datasets with documented instructions, reviewer controls, and batch-level QA evidence.
03
Speech and document AI
Test OCR, extraction, layout, transcription, metadata, recording quality, and recognition behavior against Hebrew-specific requirements.
Working principle
Operational clarity is part of the deliverable.
The work is structured around defined inputs, explicit review rules, controlled handoffs, and outputs that another team can inspect. The goal is not simply to return data—it is to make the quality decision understandable.
See how projects are controlledSelected work
Inspect the deliverable, not just the service description.
Public samples use synthetic or specifically cleared demonstration data and show how quality rules, defects, and acceptance evidence are documented.
Featured sample
Hebrew LLM evaluation report
A structured evaluation artifact covering rubric design, failure classification, reviewer evidence, and acceptance decisions.
Open sample reportOCR defect analysis
Word accuracy, critical-field extraction, table accuracy, and reading-order defects documented separately.
Open sampleAnnotation guideline
Label definitions, Hebrew-specific review checks, edge cases, and acceptance thresholds in a reusable reviewer specification.
Open sampleOperating method
From ambiguity to controlled production.
01 / Scope
Define
Use case, data, permissions, formats, metrics, and acceptance criteria.
02 / Rules
Specify
Collection, annotation, evaluation, formatting, and reviewer instructions.
03 / Pilot
Test
Run a controlled batch and expose ambiguity before scale.
04 / Review
Calibrate
Measure quality, resolve disagreements, and revise the specification.
05 / Production
Deliver
Recurring batches with QA evidence and acceptance summaries.
Specialist-led delivery
Native Hebrew judgment, structured with software QA discipline.
HebrewAITraining is led by Shoham B., based in Beersheba, Israel and serving international AI companies, data vendors, and enterprise teams. The operating approach combines native-language review with documented quality criteria, data validation, and acceptance testing.
FocusHebrew AI data, evaluation, and QA
DeliveryDirect engagement or optional marketplace contracting
LocationIsrael / international clients
Operational confidence
Quality and data handling should be inspectable too.
Bring a Hebrew AI quality problem.
Send the use case, data type, approximate volume, and target outcome. The first response will focus on scope, evidence, and the smallest useful next step.
Discuss a project