workob in

First-generation job seekers, especially 12th-pass and ITI/trade-certificate holders, often know their skills and experience but struggle to convert them into a job-ready profile. At the same time, formal job and apprenticeship listings use eligibility requirements written in language that can be difficult to understand.

The information exists on both sides, but the translation layer between the job seeker and the labour market is missing.

KaamSetu addresses this gap by helping a job seeker turn a simple voice description into a structured profile, understand which opportunities they qualify for, identify missing skills, discover relevant courses, create a resume, and prepare for interviews.

KaamSetu is an AI-powered employment and career guidance platform built around a simple idea: “Apni kahani batao. Apna agla mauka pao.”

A user can describe themselves through a short voice note in Hindi, Hinglish, or English. KaamSetu converts the voice input into a structured profile containing education, certifications, skills, experience, location, and availability.

The profile is then matched against a structured opportunity snapshot of jobs and apprenticeships. Instead of only showing a match score, KaamSetu explains why the user qualifies, what requirements are missing, and why other opportunities were excluded.

It also translates formal eligibility requirements into simple language, identifies skill gaps and recommends relevant courses.

Alongside formal jobs and apprenticeships, KaamSetu includes a “Local Kaam” section where users can discover local work opportunities such as electrician, plumber, technician, helper, delivery, retail and other entry-level work.

Finally, KaamSetu generates a one-page bilingual resume and role-specific interview questions with suggested answers.

The goal is not just to find a job, but to help a first-generation job seeker understand their options and take the next step with confidence.

React, TypeScript, Vite, Tailwind CSS, shadcn/ui, Node.js/Express, Claude API, structured matching logic, browser-based voice input, and a seeded opportunity dataset.

Claude is used for profile understanding, skill interpretation, plain-language explanations, resume generation, and interview preparation, while deterministic logic is used for eligibility and opportunity matching to keep results grounded and explainable.

Claude is used for profile understanding, skill interpretation, plain-language explanations, resume generation, and interview preparation, while deterministic logic is used for eligibility and opportunity matching to keep results grounded and explainable.