Toby: AI Hiring Manager Mock Interviews, Pay-Per-Minute Without Subscriptions

Interview preparation has long relied on memorizing question banks and rehearsing in front of a mirror. Toby, launched on Product Hunt, aims to change that — paste in a job description, and an AI hiring manager researches the company, maps out the interview rounds, and then conducts a mock interview with you over a Meet-, Teams-, or Zoom-style video call.

How It Works

The core interaction in Toby starts with a job description (JD). Once the user pastes a JD into the system, the AI researches the target company and "maps every interview round" — meaning it doesn't just throw out generic behavioral questions, but tries to reconstruct the structure of rounds likely to appear in that company's real hiring process.

The mock interview takes place as a video call, with an interface that mimics Google Meet, Microsoft Teams, or Zoom. This is worth noting: most interview practice tools stay at the level of chat-box Q&A, whereas Toby builds one of the pressure sources — the immediacy of a video call — into the practice scenario.

After the interview, users receive a scored debrief along with a "strengthened version" for each answer. The product description specifically emphasizes that these improved answers are "built only from what you actually said," rather than applying template phrasing.

Real Mode and Coach Mode

Toby offers two practice modes, corresponding to different training goals.

Real mode runs the full process without interruption, staying as close as possible to the continuous pressure of an actual interview. Coach mode offers hints at key moments and allows users to retry the current question. This "one-shot vs. redoable" dual-mode design effectively builds the feedback loop of deliberate practice into the product: first use Coach mode to break down weaknesses, then use Real mode to test overall performance.

The product is developed by Rob Kealey, earned 100 upvotes and 8 comments on Product Hunt, ranked 8th for the day, and is categorized under the Hiring, Artificial Intelligence, and Career tags.

Deliberate practice, proposed by psychologist Anders Ericsson, refers not to simple repetition but to training with clear goals, immediate feedback, and repeated correction targeting weaknesses. It emphasizes that the practitioner stays in a difficulty zone slightly beyond their current ability, and receives actionable corrective information after each attempt. Repetition without feedback — such as reciting answers in front of a mirror — often just reinforces existing bad habits. Toby's dual-mode design productizes this loop: Coach mode gives hints at key moments and allows retries, effectively front-loading "feedback" into the practice process; Real mode removes interruptions, testing overall performance without external support.

Pricing: Pay by the Minute, No Subscription

Toby's pricing model is rather unusual: 50 pence (50p) per minute, first 5 minutes free, and no subscription.

For interview preparation — a naturally "phase-based" need where you might cram intensively for two weeks and then not use it for months — subscriptions often lead to waste. Pay-per-minute means cost is directly tied to actual usage time. However, this model also carries a hidden psychological cost: users may get distracted during practice because "the billing clock is running," especially during long mock interview sessions. Whether the 50p/minute rate is worth it depends on how long a user keeps a full mock interview.

Subscription and usage-based pricing are two fundamental billing structures for software products. Subscription spreads revenue across a cycle, with users paying for "continuous availability" — better suited to high-frequency, long-term tools; usage-based pricing ties cost to actual consumption, with users paying only for what they truly use. For scenarios with large fluctuations in usage frequency, usage-based pricing is generally fairer, but it also brings uncertainty — users find it hard to estimate total costs, and billing anxiety can easily arise during use. Interview preparation happens to be a classic pulse-demand scenario: intensive use in a short period, then long idle stretches.

From Tool to Sparring Partner

The interview preparation tool market has long been dominated by static content: question banks, interview experience posts, recorded courses. The problem with these resources is the lack of feedback — you don't know how your answers sound to an interviewer.

Toby positions AI as a "sparring partner" rather than a "reference library," with scoring and answer rewriting forming a feedback loop. Its difference from general-purpose chatbots lies in scenario constraints: it incorporates company research, interview round mapping, and formalized processes like video calls. Whether these constraints significantly improve practice outcomes still lacks independent verification, but its product approach points to a clear judgment — the bottleneck in interview performance is usually not knowledge reserve, but expression and on-the-spot handling.

For those preparing for interviews, Toby offers a low-barrier (first 5 minutes free) way to test the waters. Two questions genuinely need verification: whether the AI-generated questions based on the JD are close enough to the depth of the target company's real interviews, and whether the scoring system can provide differentiated feedback rather than generic encouragement.

Behavioral interviews are a common questioning method in hiring, where interviewers ask candidates to use specific experiences to explain how they handled certain situations, with the typical prompt being "give me an example of how you handled a conflict." The underlying assumption is that past behavior reasonably predicts future performance. Therefore, the key to measuring the quality of such interview answers usually lies not in whether the viewpoint is novel, but in whether the structure is clear, the details are credible, and the results are quantifiable. The common STAR framework (Situation, Task, Action, Result) is designed precisely to organize such answers. Whether an AI tool can infer from a job description the behavioral dimensions preferred by that company directly determines how targeted the mock questions are.

One-line Summary

Toby uses AI to simulate interviews in video-call-like sessions, charged by the minute, attempting to turn interview prep from question memorization into sparring.

Paragraph Summary

Toby is an AI interview simulation tool launched on Product Hunt. After users paste a job description, the AI researches the target company and maps out interview rounds, asking questions in simulated Meet, Teams, or Zoom video calls. The product offers two practice modes — Real mode (full process without interruption) and Coach mode (hints at key moments, retryable) — and after the interview outputs a scored debrief along with strengthened answers built only from what the user actually said. Pricing is 50p/minute, first 5 minutes free, no subscription, matching the phase-based usage pattern of interview preparation. It earned 100 upvotes on Product Hunt and was developed by Rob Kealey.