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Visa Sponsor Intelligence

Database of H-1B employer sponsorship patterns, selection rates, and wage levels

EducationInternational students and workers job-hunting in the US who need H-1B sponso...
The Gap

Job seekers targeting H-1B sponsoring companies have no way to compare employers' lottery success rates, typical wage levels offered, or sponsorship track records

Solution

Aggregate the structured data people already share (Result, Company/Industry, Location, Wage Level) into a searchable database. Enrich with LCA disclosure data from DOL. Let job seekers filter by industry, location, company size, and historical selection rates

Revenue Model

Subscription - $19/mo or $149/year for full access. Freemium tier with limited searches. Affiliate revenue from immigration attorney referrals

Feasibility Scores
Pain Intensity9/10

This is career-and-life-altering for the target audience. A bad employer choice can mean deportation, years of wasted effort, or tens of thousands in lost wages from low wage-level offers. The Reddit thread with 435 upvotes and 84 self-reports in a niche sub proves people are DESPERATE for this data. They're manually crowdsourcing it because nothing else exists. Pain doesn't get much more intense than 'will I be allowed to stay in this country?'

Market Size6/10

TAM is real but bounded. ~400K-500K unique H-1B registrants per year, plus ~200K international students actively job-hunting = ~600K-700K potential users annually. At $149/year, theoretical max ~$100M, but realistic penetration of 1-3% = $1M-$3M ARR ceiling for a bootstrapped product. This is a strong niche business, not a venture-scale market. That's fine — it's perfect for a solo founder.

Willingness to Pay8/10

This audience already pays $1,500-$5,000+ for immigration attorneys, $200-$500 for premium job boards, and spends heavily on credential evaluations and USCIS fees. $19/month is a rounding error compared to the cost of a bad H-1B outcome. International workers in tech also tend to have above-average income. The key proof: people are manually doing this work for free in Reddit threads — they'll absolutely pay for a polished, persistent, enriched version.

Technical Feasibility9/10

DOL LCA disclosure data is publicly available in bulk CSV/Excel downloads. USCIS publishes registration and approval statistics. Reddit/forum scraping for crowd-sourced data is straightforward. The core product is: ingest CSVs + build a search/filter UI + add crowd-sourced overlay. No ML needed for MVP. A competent solo dev with Python/Next.js can build this in 3-4 weeks. The hard part is data cleaning and entity matching (company names vary), not technical complexity.

Competition Gap8/10

The critical insight: EVERY existing tool shows LCA filing data (who APPLIED). NONE show lottery outcome data (who GOT SELECTED and at what rate). This is the gap. The Reddit crowd-sourcing proves demand for outcome-level data that simply doesn't exist in any product. Existing tools also have atrocious UX and no employer comparison features. You're building the 'Glassdoor for H-1B sponsorship' — the analogy itself sells the product.

Recurring Potential7/10

Strong annual recurring during H-1B season (Oct-April), but usage may be seasonal/bursty. A user who gets selected may churn. Mitigation: expand to green card tracking, OPT/STEM OPT extensions, and employer wage negotiation data to increase lifecycle value. The annual cycle creates natural re-engagement: 'FY2028 data is now available.' Expect ~60% annual retention as users either succeed or re-enter the lottery.

Strengths
  • +Extreme pain point with proven demand — crowd-sourced data in Reddit threads is your validation AND your initial dataset
  • +No existing product combines DOL data with lottery outcome intelligence — you own a unique data layer
  • +Technically simple MVP with publicly available government data as the foundation
  • +Audience has high willingness to pay and is concentrated in a few online communities (r/h1b, Blind, international student forums) making acquisition cheap
  • +Natural network effect: each user who reports their outcome makes the database more valuable for everyone
Risks
  • !Seasonal demand — H-1B lottery cycle drives ~70% of engagement in a 6-month window, requiring either diversification or acceptance of cyclical revenue
  • !Policy risk — if USCIS changes the lottery system dramatically (e.g., moves to merit-based), your core value prop shifts. This is also an opportunity: every policy change drives demand for new analysis
  • !Data quality — crowd-sourced outcome data can be gamed or inaccurate. Entity matching (company names) across DOL data and user reports requires careful deduplication
  • !Incumbent SEO — MyVisaJobs and h1bdata.info own high-value keywords. You'll need community-driven growth (Reddit, WhatsApp groups, university career centers) rather than SEO to start
Competition
MyVisaJobs

Aggregates LCA and PERM data from DOL to show H-1B sponsor history, salary data, and green card sponsorship by employer. Offers job listings tied to sponsoring companies.

Pricing: Free with ads; premium features unclear/limited monetization
Gap: No lottery selection rate data, no crowd-sourced outcome tracking, clunky UX stuck in 2012, no community engagement, no real-time FY cycle tracking. Purely backward-looking LCA data — tells you who FILED, not who GOT SELECTED.
H1BGrader

Grades employers on H-1B sponsorship likelihood based on historical LCA filings. Simple letter-grade system

Pricing: Free
Gap: Extremely shallow — just a letter grade with no drill-down. No lottery outcome data, no wage level analysis, no filtering by location/industry, no trend tracking. A toy, not a tool.
Open H1B (h1bdata.info)

Searchable database of H-1B LCA disclosure data. Lets users look up salaries and job titles by employer and location.

Pricing: Free with ads
Gap: Only shows LCA filings (applications), NOT lottery outcomes or selection rates. No employer comparison tools, no analytics dashboard, no community data layer. Cannot answer 'what are my ODDS with this employer?'
Immihelp / VisaGrader tools

Immigration portal with various visa trackers, forums, and community-sourced case status data. Includes H-1B filing trackers.

Pricing: Free; monetized via ads and attorney referrals
Gap: Data is unstructured forum posts, not a queryable database. No employer-level analytics, no lottery rate comparisons, no wage level insights. Community data exists but is buried in threads — exactly the problem your idea solves.
Blind / Reddit r/h1b (crowd-sourced forums)

Not a product per se, but the de facto source of real-time H-1B lottery outcome data. Users self-report results with employer, wage level, and location.

Pricing: Free
Gap: Data dies in threads — not searchable, not structured, not persistent year-over-year. No filtering, no aggregation, no trend analysis. Every FY cycle, the community rebuilds this dataset from scratch in a new Reddit thread. This is the #1 gap your product fills.
MVP Suggestion

Week 1-2: Ingest DOL LCA disclosure data (publicly available CSVs) into a Postgres database. Build a Next.js frontend with company search, salary filtering, and location views. Week 3: Add a simple crowd-sourced lottery outcome submission form (Result | Company | Industry | Location | Wage Level — mirror the Reddit format). Seed it with data scraped from the Reddit thread. Week 4: Add freemium gating (5 free searches/month, unlimited for subscribers), Stripe integration, and an immigration attorney referral directory. Launch on r/h1b, r/immigration, and international student WhatsApp/Discord groups during the FY2028 registration window.

Monetization Path

Free tier (5 searches/month, basic company lookup) -> Pro at $19/mo or $149/year (unlimited searches, lottery outcome analytics, employer comparison, wage level breakdowns, alerts for new data) -> Affiliate revenue from immigration attorney referrals ($50-$200 per qualified lead) -> Enterprise tier for immigration law firms wanting bulk data access ($499/mo) -> Eventually: sponsored employer profiles ('We sponsor H-1B' badge program for recruiting)

Time to Revenue

4-6 weeks to MVP and first paying users if launched during H-1B season (Oct-April). The FY2028 registration window (likely March 2027) is the ideal launch target, but you can launch now with historical data and build the crowd-sourced layer ahead of the next cycle. First $1K MRR achievable within 2-3 months of launch given the concentrated, high-intent audience.

What people are saying
  • Structured reporting format requested (Result | Company | Location | Wage Level) shows demand for this exact dataset
  • High engagement thread focused on employer-level outcomes
  • 84 comments of crowd-sourced data that currently dies in a Reddit thread