From AI Recruiting Concept to MVP and Funding

An HR tech startup developing an AI platform for HR professionals and hiring managers. Its goal was to bring candidate information and interview findings into a connected workflow, automate repetitive assessment tasks, and give teams a clearer basis for comparing candidates.

The founders approached us with a product concept and needed technical expertise to build an MVP, demonstrate its capabilities, and seek funding for further development.

The product needed to support several connected stages of recruiting. It would analyze CVs against job requirements, generate candidate-specific interview questions, transcribe interviews, and assess responses. These functions had to work together so information collected at one stage could inform the next.

The platform also needed to make its recommendations understandable. A candidate score alone would provide limited value unless hiring teams could see the information behind it. The startup needed a working product that demonstrated both the assessment capabilities and a practical way for users to review the findings.

We developed an AI recruiting MVP that combines CV analysis, interview support, and candidate recommendations. The platform analyzes uploaded CVs against job descriptions, assigns job-fit scores, and generates interview questions based on the candidate’s profile and the role.

During the interview workflow, the platform records and transcribes conversations, then analyzes the relevance and clarity of responses. This adds interview findings to the information available from the CV, supporting a more detailed comparison between candidates.

The hiring assistant presents candidate rankings and recommendations with explanations. Hiring teams can review the findings and their basis when deciding whom to interview or hire.

Connected Assessment Architecture

We defined an architecture to support CV analysis, question generation, transcription, and interview assessment within the same platform. We considered how these tasks would exchange information while keeping the user experience manageable. The design also accounted for the need to handle growing volumes of users and candidate data as the product developed.

Speech Recognition for Technical Interviews

Interview conversations included specialized terminology that the transcription system needed to capture accurately. We evaluated existing speech-to-text models against this requirement before selecting a suitable option. This avoided the time and resources needed to develop a custom model while addressing the language used by the startup’s target audience.

Candidate Analysis and Scenario Testing

We developed prompts that guided the AI to compare candidate profiles and job descriptions against relevant requirements. Interview analysis also needed to account for conversational language and the meaning of responses. We tested the assessment workflow across different scenarios and refined prompts and outputs to improve consistency.

Clear Explanations for Hiring Teams

We designed the presentation of recommendations alongside the assessment functionality. Candidate rankings and job-fit scores were accompanied by explanations, with accessible wording and interactive visualizations. This gave users a way to examine the basis of a recommendation and combine the platform’s findings with their own judgment.

The startup launched a working MVP and used the product demonstration to support its fundraising efforts. The company secured funding to continue development, while our combined engineering and recruiting experience informed further refinement of the platform.

AI recruiting MVP launched

The founders moved from a concept to a product that demonstrated CV analysis, interview support, and candidate recommendations within a connected workflow.

Funding secured for continued development

The product concept and demonstration resonated with investors, enabling the startup to obtain funding for the next stage of development.

Recommendations users could examine

The platform presented candidate rankings with explanations and visualizations, giving hiring teams information they could review when assessing the AI’s recommendations.