Live products. Real users. Real revenue.

The problem

47% of EB1A green-card petitions are denied — not because the applicant isn't extraordinary, but because the evidence isn't framed in USCIS regulatory language. Lawyers charge $250–$500/hr to fix it, and most self-petitioners can't afford that.

What we shipped

• A RAG system trained on 5,000+ published USCIS AAO decisions that matches a petitioner's evidence to the criteria most likely to win
• AI petition-section drafting, reference-letter generation and RFE-response assistance in USCIS-compliant language
• Four-tier subscription product (Free → Elite) with Stripe billing, account management, and a 90-day money-back guarantee
• Real-time USCIS news pipeline ingesting official government sources so guidance never goes stale

Why it’s hard

• Claude Opus integration with custom prompt engineering per AAO criterion
• Curated and versioned legal dataset, not a generic LLM wrapper
• Subscription pricing 60–80% below attorney models, with margin that still works at scale

Since launch (as published by Lumova)

applicants served

1,200+

applicants served

countries

80+

countries

platform approval rate

53%

platform approval rate

from 340+ verified reviews

4.9 / 5

from 340+ verified reviews

The problem

Applying to NHS, Civil Service, local-authority and university roles used to burn 3–4 hours per application. Miss one essential criterion and the application is binned — usually without feedback.

What we shipped

• Gap-analysis engine that scores a candidate's profile against every essential and desirable criterion on the person specification
• AI supporting-statement generator that writes STAR-structured examples to the exact character counts UK public-sector forms demand
• NHS 6Cs alignment scoring, Civil Service-grade calibration and ATS-safe DOCX/PDF export
• Interview predictor with STAR builder and role-specific practice scoring, end-to-end from application to offer

Why it’s hard

• Deeply domain-specific: NHS bands, Civil Service grades, police/fire/armed-forces conventions — no margin for 'close enough'
• Calibrated on hundreds of hours of applied research per sector
• UK English, ATS-safe formatting, and character-budgeted output the big LLMs get wrong out of the box