Reclamo AI expands direct access to wage rights for New York workers
Justicia Lab, Pro Bono Net's innovation incubator, and the New York Legal Assistance Group (NYLAG) have launched Reclamo AI - a multilingual, mobile-first assistant that helps low-wage and immigrant workers in New York prevent, assess, and document workplace violations.
First introduced in 2022 to screen and file wage theft complaints via nonlawyer advocates, the original tool has already supported more than $1.5 million in filed claims. The new release lets workers use it directly by text or voice in any smartphone browser or through WhatsApp, with content optimized in English and Spanish and available in 50 additional languages.
The platform now covers the full scope of New York State employment law and prioritizes industries with high rates of violations: construction, hospitality, domestic work, retail, and child and elder care.
Why it matters for legal practitioners
Wage theft remains widespread in New York. More than 127,000 New Yorkers have been affected, with losses in the millions, disproportionately impacting immigrants, women, and communities of color.
Barriers persist: limited knowledge of wage and hour law, confusing complaint processes, language access gaps, and overwhelming demand on legal aid. Many valid claims never get filed. Reclamo AI helps close that gap.
What Reclamo AI delivers
- Plain-language education on state and federal wage laws, including minimum wage, overtime, and unlawful deductions.
- Guided assessment to document facts and surface potential claims faster.
- Employer lookups to see prior wage theft violations.
- Connections to pro bono legal services and trained nonlawyer advocates.
- Direct worker access via smartphone browser or WhatsApp, using text or voice.
- English, Spanish, and 50 more languages to meet workers where they are.
- Coverage focused on sectors with the highest incident rates.
Data sources and quality control
Reclamo AI relies on vetted materials compiled by NYLAG attorneys, data from Documented NY's Wage Theft Monitor (35,000+ confirmed claims), and additional proprietary resources developed by Justicia Lab and partners. The approach prioritizes verified legal information over generic internet content.
Privacy and security, by design
User inputs are stripped of personally identifiable information, anonymized, and deleted every 48 hours. The system uses enterprise AI models and does not use or share user data for model retraining.
From advocate-first to worker-first
The 2022 version was built for organizers and staff at worker and employment centers, with early scale in the construction and building trades. The latest release shifts access directly to workers while keeping pathways to advocates and legal aid intact.
Voices from the field
Rodrigo Camarena, director at Justicia Lab, noted that many workers feel sidelined, and this tool reminds them they have a voice and partners who will stand with them.
Mel González, who leads NYLAG's Employment Law Project, underscored the reality: legal service organizations are overloaded. With AI-enabled platforms grounded in worker-centered design, more low-wage and immigrant workers can receive accurate, timely legal information and support.
Practical takeaways for legal teams
- Use Reclamo AI as an intake accelerator: pre-education, basic screening, and documentation before counsel review.
- Leverage employer history checks to spot patterns and inform strategy.
- Embed links or QR codes in outreach to community partners, clinics, and unions to streamline referrals.
- Train nonlawyer advocates to use the tool in clinics, then escalate complex matters to attorneys.
- Pair Reclamo AI with state complaint processes to reduce errors and incomplete filings. For reference: How to file a wage claim (NYS DOL) and the federal Wage and Hour Division.
Reclamo AI will continue to evolve through feedback loops with workers and advocates. For legal practitioners, it's a practical, privacy-conscious channel to extend rights education, triage faster, and reserve scarce attorney time for higher-complexity matters.
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