Expert Review Workflow
A structured interface for qualified bilingual professionals to approve, edit, or flag each translation — with HTML rendering, clinically sensitive flagging, and full audit trail.
A structured workflow for qualified bilingual professionals to review AI-assisted translations and generate auditable attestation documentation — designed to support LEP language access compliance obligations.
Review documentation designed to support compliance with
Section 1557 / ACA
Prohibits language discrimination in health programs and requires meaningful access for Limited English Proficient individuals.
45 CFR Part 92
Defines qualified translator standards and mandates expert human review of AI-generated translations used in health communications.
Title VI
Prohibits national origin discrimination — including language barriers — in all programs and activities receiving federal financial assistance.
Executive Order 13166
Requires recipients of federal financial assistance to provide LEP persons with meaningful access to all programs and activities.
Human Review Standard
Requires a qualified human translator to review machine-generated translations before use in critical health communications.
ATA Standards
American Translators Association professional ethics: role disclosure, accuracy affirmation, impartiality, and client confidentiality.
Purpose-built for qualified bilingual healthcare professionals reviewing machine-assisted translations in clinical and research settings.
A structured interface for qualified bilingual professionals to approve, edit, or flag each translation — with HTML rendering, clinically sensitive flagging, and full audit trail.
Pattern-aware auto-confirmation: approve a recurring phrase once and every identical instance is confirmed — eliminating redundant review without sacrificing expert oversight.
QR-verified PDF certificates with full attestation text, review statistics, regulatory framework references, and a unique, publicly accessible verification URL.
Expert reviewers on this platform validate purpose-built translations — not generic AI output. Our proprietary pipeline produces healthcare-calibrated text engineered for the clinical and cultural context of each target population.
Vocabulary calibrated for medical terminology, drug references, and health administration language in the target locale — not generic consumer translation.
Accounts for regional language variation within target communities — not a single standardized form that may be foreign to the patient population being served.
Output complexity matched to the literacy range of the intended patient or participant population — meeting the meaningful access standard in 45 CFR § 92.4.
Reflects current, community-accepted usage rather than academic or formal translation conventions — appropriate for the actual population being served.
Proprietary methodology · Pipeline documentation available to covered entities under NDA
From expert onboarding to verifiable attestation documentation in three steps.
Expert creates a verified profile with qualifications, language pairs, institution, and professional certifications — forming the basis of the attestation record.
Work through each AI-assisted translation: approve accurate items, submit edits where needed, and flag content requiring clinical attention.
A signed attestation certificate is issued with a unique verification URL, downloadable PDF, and QR code — on file and publicly verifiable.
Every review generates documentation aligned with applicable regulatory frameworks — accessible via a unique public URL with no account required.
Each certificate is accessible at a unique, publicly accessible verification URL and includes the expert's full attestation, review statistics, and regulatory framework documentation. Covered entities should retain certificates per applicable federal recordkeeping requirements (minimum 6 years).
Verify a certificate number →Tell us about your project and we'll follow up to discuss scope, languages, and timeline. All inquiries are handled by our team directly.