Fake job offers and shell-company sponsors flood the H1B lottery, reducing legitimate applicants' odds and creating distrust in the system.
Aggregate public LCA/H1B data, employer reviews, wage level analysis, and company verification signals into a trust score for each sponsoring employer. Applicants can check employers before accepting offers; attorneys can use it for due diligence.
Freemium — free basic employer lookup, $9.99/mo subscription for detailed fraud-risk scores, wage benchmarking, and sponsor history reports. B2B tier for law firms at $99/mo.
H1B applicants risk their legal status, career trajectory, and tens of thousands in legal fees on sponsor legitimacy. A fraudulent sponsor can result in deportation, wasted lottery selections, and years of lost time. The Reddit signals show visceral frustration. This is hair-on-fire pain for the affected population — not a nice-to-have, but a protect-my-future necessity.
~750K-800K unique H1B registrants per year, plus ~15,000+ immigration attorneys in the US, plus corporate compliance teams. TAM for B2C: if 10% of registrants pay $9.99/mo for even 3 months, that's ~$22M ARR. B2B attorney tier adds another $5-10M. Not a billion-dollar market, but a solid $30-50M TAM niche with expansion potential into other visa categories (L1, EB, OPT employers).
H1B applicants already spend $5K-15K on attorneys and $2K-4K on lottery registration fees. $9.99/mo is trivial relative to stakes. Immigration attorneys routinely pay for LexisNexis, Westlaw, and compliance tools at $100-500/mo. The friction is that free data exists (H1BData.info) — you must prove the INTELLIGENCE layer is worth paying for beyond raw data. Pain signals are strong enough to convert.
All core data is publicly available: LCA disclosures (DOL), USCIS employer data hub, SEC/state business registrations, DOL enforcement data. A solo dev can aggregate these APIs/datasets, build a scoring algorithm (wage level distribution, company age, petition volume patterns, registered agent analysis), and ship an MVP web app in 4-6 weeks. No proprietary data needed. Python/Django or Next.js + PostgreSQL is sufficient.
This is the killer insight: NOBODY is doing fraud scoring. Every competitor is a data browser. H1BData shows salaries. MyVisaJobs ranks by volume. H1BGrader grades on approval rates. None of them answer the question 'Is this employer likely legitimate?' No one cross-references wage levels against market rates, checks company registration age, flags unusual petition patterns, or integrates DOL violation history. The trust-score concept is a genuine whitespace.
Moderate challenge. H1B applicants have seasonal/cyclical usage (heavy Oct-March during lottery season, lighter otherwise). Churn risk is high once someone gets placed. However: attorneys and compliance teams are year-round users, monitoring clients is ongoing. Adding alerts (employer status changes, new enforcement actions) and expanding to green card/PERM tracking improves retention. B2B is the real recurring revenue anchor.
- +Massive competition gap — no one does fraud/trust scoring despite obvious demand
- +All required data is publicly available, making MVP technically straightforward
- +Extremely high-stakes decision for users creates strong willingness to pay
- +Regulatory tailwinds — USCIS crackdowns and political scrutiny increase demand for transparency
- +Clear B2B expansion path to immigration attorneys and compliance teams at higher price points
- +Strong SEO/content marketing potential around H1B fraud topics (high search volume, low competition)
- !Seasonal usage pattern — H1B lottery is cyclical (Oct-March peak), leading to potential churn outside season
- !Scoring accuracy liability — if you flag a legitimate employer as risky (false positive), you could face legal threats or reputation damage. Needs clear disclaimers and methodology transparency
- !Data freshness dependency — LCA/USCIS data has lag (weeks to months), meaning real-time accuracy is limited
- !Political risk — H1B program rules change frequently; a major policy shift (e.g., eliminating the lottery) could shrink the market overnight
- !Free data competition — power users can DIY the analysis from public sources; your moat is the scoring algorithm and UX, not the data itself
Free searchable database of H1B salary data from LCA filings. Users can look up employers, job titles, and salaries by location.
H1B sponsor database with employer rankings, green card tracker, salary comparisons, and job listings. Also covers PERM and labor condition data.
Grades H1B employers on approval rates, processing times, and petition outcomes. Provides employer report cards.
Official government portal showing H1B petition data by employer including approvals, denials, and continuations.
General employer review platforms where employees rate companies. Some H1B-specific discussions surface in reviews.
Simple web app: employer search bar → returns a trust score (A-F grade) with breakdown. Score built from: (1) wage level distribution vs. market (Level 1 abuse flag), (2) company age and registration status, (3) petition volume vs. company size ratio, (4) historical approval/denial rates, (5) DOL violation history. Free tier: employer name + grade. Paid tier: full breakdown, historical trends, PDF reports for attorneys, and email alerts on employer status changes. Ship with 50K+ employers pre-scored from DOL LCA data.
Free employer lookup with basic grade (drives SEO traffic and virality in H1B forums/Reddit) → $9.99/mo individual subscription for detailed reports, wage benchmarking, and alerts → $99/mo law firm tier with bulk lookups, client-facing branded reports, and API access → $499/mo enterprise tier for large immigration law firms and corporate compliance teams with white-label reports and batch screening
4-6 weeks to MVP launch, first paying customers within 2-3 months. Target October-November launch to catch FY2027 H1B lottery season (registrations typically open in March). Reddit/H1B forums are the distribution channel — post value-add content with employer analyses to drive organic traffic. First $1K MRR achievable within 3-4 months of launch.
- “Fake job offers and wage levels should get scrutinized in depth”
- “making sure all fake applications are eliminated”
- “Hope they're scrutinizing fake job offers and such cases”
- “filter out the fake ones”