Answerly

Turn unfamiliar AI interview formats into confident conversations.
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Practice realistic AI interviews and receive clear, actionable feedback.
Interview process

What it does

Answerly is an agentic AI platform that helps people prepare for modern job interviews.

The platform brings the main parts of interview preparation into one place. Users can learn important topics, practise individual questions, complete realistic mock interview sessions, and track their progress over time.

Answerly does not only generate random questions. It uses the user’s selected role, goals, previous answers, and weak areas to create a more personalised preparation process. After each practice session, the user receives feedback on the quality, structure, clarity, and relevance of their answers.

The platform includes:

role-specific interview questions;
topic-based learning materials;
an AI-powered practice room;
personalised feedback;
progress and activity tracking;
recommendations for what to practise next.

The goal is to help users understand not only what they already know, but also what they still need to improve before a real interview.

Answerly is especially relevant now because more companies are introducing AI interviews, recorded screening stages, automated assessments, and structured interview platforms. Many candidates are technically prepared for a role but are not familiar with this new interview format. Answerly gives them a safe environment where they can practise and build confidence before the real process begins.

How we built it

We started by dividing the preparation process into three main areas: learning, practising, and tracking progress.

The learning section allows users to review interview topics before answering questions. The questions section is designed for focused practice, where users can work on one topic or question at a time. The practice room creates a more realistic interview experience by presenting a structured sequence of questions and reacting to the user’s answers.

We added an AI layer that can generate questions, analyse responses, provide feedback, and recommend the next steps. Instead of treating every answer as simply correct or incorrect, the system looks at several aspects of the response, including clarity, structure, relevance, confidence, and completeness.

We also created a tracking system that stores completed sessions and displays the user’s progress. This allows users to see which topics they have already covered, how their results are changing, and which areas require more practice.

The main user flow is simple:

The user selects a role or interview goal.
Answerly recommends relevant topics and questions.
The user studies or practises selected material.
The user completes a mock interview in the practice room.
The platform analyses the answers and provides feedback.
The progress tracker shows results and recommends the next step.

During the hackathon, we focused on making this entire flow understandable and usable. We wanted the project to feel like one connected product rather than a collection of unrelated AI features.

Challenges we ran into

One of the main challenges was deciding how the AI should behave.

Generating interview questions is relatively simple, but creating an AI interviewer or coach is much more difficult. The system needs to remember the context of the session, understand the user’s previous answers, ask relevant follow-up questions, and provide feedback that is actually useful.

Another challenge was evaluating interview answers. Many answers cannot be classified as completely right or wrong. A candidate may understand the topic but explain it poorly. They may give too much unnecessary information, miss the central point, or provide a correct answer without enough detail.

Because of this, we needed to think about feedback in several categories instead of returning one general score.

We also had to limit the scope of the project. We had ideas for voice analysis, CV-based questions, company-specific interviews, job-description analysis, and advanced performance statistics. However, the hackathon gave us limited time, so we focused on the features that best demonstrated the core value of Answerly.

Designing the interface was another challenge. Interview preparation can already feel stressful, so we wanted the platform to remain simple and easy to navigate. We avoided adding too many controls, graphs, and technical settings that could distract users from practising.

Accomplishments that we're proud of

We are proud that Answerly became more than a basic AI chatbot.

The platform combines learning, question practice, realistic interview sessions, feedback, and progress tracking into one connected experience. Users are not left with a list of generated questions. They receive a clearer preparation path and guidance about what to do next.

We are also proud of the personalised approach. The platform can adjust the preparation process based on the selected role and the user’s previous performance. This makes the experience more useful than giving every candidate the same set of questions.

Another important accomplishment was building a complete user journey during the hackathon. A user can select a goal, practise, complete a session, receive feedback, and review progress without leaving the platform.

The project also addresses a real change in recruitment. AI-powered screening and automated interviews are becoming more common, but many candidates do not know how to prepare for them. Answerly gives users a practical environment where they can experience this type of interview before facing it during a real recruitment process.

Most importantly, we created a strong foundation for a product that could continue growing after the hackathon.

What we learned

The biggest thing we learned is that AI becomes much more useful when it is part of a structured workflow.

Generating content is not enough. A useful interview platform needs to understand context, organise the preparation process, remember progress, provide meaningful feedback, and clearly show the user what to do next.

We also learned how personal interview preparation can be. Two people applying for the same role may need completely different types of support. One person may struggle with technical knowledge, while another may understand the topic but have difficulty explaining their thoughts clearly.

This showed us that personalisation should remain one of the most important parts of Answerly.

The hackathon also taught us how important prioritisation is. We had many possible features, but trying to build all of them would have resulted in an unfinished product. By focusing on the central user flow, we were able to create a more complete and understandable experience.

We also gained practical experience in connecting AI features with a real interface, managing user progress, designing feedback systems, and turning a broad idea into a working prototype.

What's next for Answerly

The next step is to make Answerly more realistic and more personalised.

We plan to add interview preparation based on real job descriptions. A user would be able to provide a vacancy description, and Answerly would generate a preparation plan based on the required technologies, responsibilities, and experience level.

We also want to add:

CV-based interview questions;
voice-based interview sessions;
speech clarity and communication analysis;
dynamic follow-up questions;
company-specific interview simulations;
different interviewer personalities and difficulty levels;
detailed performance analytics;
multilingual interview preparation;
technical coding interview support;
personalised learning plans.

The practice room could also include realistic time limits, voice interaction, and recorded answers. This would make the experience closer to the automated interview systems already used by some companies.

In the future, Answerly could support the entire preparation process, from analysing a job description to completing a final mock interview.

Our long-term goal is to make interview preparation clearer and less stressful. When a user receives an interview invitation, they should be able to open Answerly, follow a personalised plan, practise in a realistic environment, and enter the interview with more confidence.