Comprehensive overview of our AI-powered evaluation system for visa sponsorship prediction, CV parsing, job readiness assessment, and company recommendations.
Surfaces a UK Skilled Worker Visa sponsorship readiness signal using sector, company, and job data.
Extracts structured information from resume text including personal info and work experience.
Evaluates candidate readiness against job requirements using the UK Standard Skills Classification.
Recommends companies based on role keywords, salary expectations, and funding data.
| Factor | Weight |
|---|---|
| Sector sponsorship rate | 35% |
| Job-role sponsorship rate | 30% |
| Company sponsorship history | 25% |
| New-Entrant or PhD adjustment | 5% |
| Salary-threshold compliance | 5% |
You are an AI-powered UK Skilled Worker Visa Sponsorship Prediction Assistant.
Given the following:
--- Sector Data ---
[Matched sector information from database]
--- Company Sponsorship History ---
[Historical sponsorship data for matched companies]
--- Job Title Sponsorship History ---
[Sponsorship rates for similar job titles]
Additional Info:
- Expected Salary: £[amount]
- New Entrant: [Yes/No]
- PhD: [Yes/No]
Instructions:
Output the following JSON object:
{
"factor_scores": {
"eligibility_tier_score": number between 0 and 100,
"sector_rate": number between 0 and 100,
"role_rate": number between 0 and 100,
"salary_compliance": number between 0 and 100
},
"probability": number between 0 and 100,
"justification": "A short, clear justification in British English explaining why the user received this probability.",
"component_scores": {
"sector_sponsorship_popularity": number between 0 and 100,
"soc_code_compatibility": number between 0 and 100,
"skill_readiness": number between 0 and 100
},
"prediction_insights": {
"confidence": "One of: Strong Likelihood, Moderate Likelihood, or Low Likelihood",
"insight": "One sentence highlighting the user's current potential",
"estimated_timeline": "e.g. '2–4 weeks' or '1–2 months'",
"percentile": "e.g. 'Top 25% of applicants in your category' or 'Top 50%'"
}
}
Apply the following weights (VSRS 4-factor system):
| Factor | Weight |
|-------------------------------------|--------|
| Eligibility tier (ISL/TSL/PhD/Gen) | 30% |
| Sector rate (Home Office data) | 35% |
| Role rate (Home Office data) | 30% |
| Salary compliance (SOC threshold) | 5% |
Eligibility tier scoring:
- ISL (Immigration Salary List / Shortage): 100
- PhD (RQF 8): 90
- TSL (Transitional Skilled List): 80
- General Threshold (RQF 6+): 70
- Subtract 10 if New Entrant
Respond only with valid JSON.The system enforces manual calculation on the backend to ensure consistency:
// Backend calculates probability using exact weights
const calculatedProbability = calculateVisaProbability(parsed.factor_scores);
if (parsed.probability !== calculatedProbability) {
console.warn(`AI probability (${parsed.probability}) differs from backend calculation (${calculatedProbability}).`);
parsed.probability = calculatedProbability;
}
// Returns the backend-calculated probability regardless of AI outputBackend Method: calculateVisaProbability
export function calculateVisaProbability(factorScores: {
eligibility_tier_score: number; // 0-100 (ISL=100, PhD=90, TSL=80, General=70)
sector_rate: number; // 0-100 from Home Office data
role_rate: number; // 0-100 from Home Office data
salary_compliance: number; // 0-100 percentage
}): number {
const weighted =
factorScores.eligibility_tier_score * 0.30 +
factorScores.sector_rate * 0.35 +
factorScores.role_rate * 0.30 +
factorScores.salary_compliance * 0.05;
return Math.max(0, Math.min(100, Math.round(weighted)));
}