A.I · AI Recruitment
We developed a minimum viable product to test the market for an AI-powered hiring platform that handles majority of admin work from recruiters
Outcome
Recruiters’ screening admin handed to the platform: resumes and job specs structured, then GPT-scored per match; 2 frontends on one core.
The recruitment market is saturated with platforms, yet fundamental inefficiencies persist. Employers spend countless hours sifting through mismatched applications, while qualified candidates often miss the right opportunities. The space between talent and the companies that need it is filled with manual processes and guesswork.
The client, a new recruitment marketplace, identified this friction as its core business opportunity. Their hypothesis was that an intelligent system could filter the noise, connecting the right people to the right roles with greater precision than human screeners alone. The challenge was not the idea, but proving its viability in a real-world application.
To validate this concept with minimal initial investment, they required a functional MVP. We were engaged to build the foundational platform, from the backend architecture to the AI-driven matching engine. Our work was to provide the technical proof needed to test their business model directly with users.
The challenge
How we solved it
Technologies
The system ingests job requirements and analyzes the text of candidate profiles and resumes. Using OpenAI models, it assesses qualifications, experience, and skills to generate a ranked shortlist of applicants for each role. This moves beyond simple keyword matching to provide employers with a prioritized list based on genuine suitability, saving significant screening time.
The platform provides two distinct interfaces served from a single backend. Employers receive a dashboard focused on managing job postings and reviewing AI-ranked candidate lists. Job seekers are given a personalized view that surfaces relevant job recommendations and allows them to track their application status. This separation ensures a focused and efficient experience for both sides of the marketplace.
How we built it
Structured every resume and job spec before the model saw it, so matching quality did not depend on the prompt. Raw CVs and job posts are inconsistent, and feeding them straight to a language model produces inconsistent scores. A processing layer normalises both into structured inputs first, and only then are OpenAI GPT models asked to score the match, which makes results comparable across candidates. One Laravel core with role-based access serves two separate React frontends, one for employers and one for job seekers, behind a RESTful API that handles documents, authentication and applications, lean enough for an MVP and clean enough to grow.
In the client's words
“The matches make sense to a recruiter reading them, which is the only test that mattered to us.”
Tell us what you're building. We'll show you how we'd approach it and what it would take to ship.