The candidate you never called back
Tuesday, 2 PM. The senior back-end developer you'd been courting for three weeks just signed with a competitor. You find out through a two-line LinkedIn message: "Thanks for the process, but I've accepted another offer."
I lived this scenario dozens of times as an account manager at a staffing agency. Candidate sends their resume on Monday. You forward it to the tech lead on Tuesday. The tech lead is mid-sprint — can't do it before Thursday. Thursday evening, they run a 45-minute technical screen on their own time. Friday, they email you their feedback. Monday, you call the candidate back. Gone.
Five days of process. In a market where strong candidates decide in 48 hours.
During my years in staffing, I tried every solution I could find. Online MCQs. Automated technical tests. HackerRank, Codility, TestGorilla. I looked at all of them. None solved my actual problem.
Why MCQs don't work
Multiple-choice tests measure one thing: the ability to pick the right answer. Knowing that the Observer pattern implements a publish-subscribe mechanism doesn't prove the candidate can use it in a real project.
I've seen junior developers ace MCQ scores because they'd memorized standard answers. And experienced seniors fail trick questions on syntax details they hadn't encountered in years.
From the candidate's perspective, it's worse. A senior developer you send a 90-minute HackerRank test on a Sunday evening? They don't take it. They remove you from their contacts. Strong candidates have options, and they choose processes that respect their time.
The fundamental issue: an MCQ evaluates declarative knowledge. What you actually need to evaluate in a developer is their ability to reason, explain their choices, and adapt to a problem they've never seen before. A multiple-choice questionnaire doesn't measure that.
How a conversational AI technical interview works
An AI interview isn't a chatbot. It's a 25-minute technical conversation that adapts to the candidate in real time.
In practice, it feels like talking to a supportive tech lead. The AI asks an open-ended question on a technical topic relevant to the role. The candidate responds. Based on the quality and depth of the answer, the AI decides whether to dig deeper into the topic, move on to another one, or rephrase to better gauge the level.
A candidate showing advanced React mastery? The AI pushes into complex state management, render optimization, and architectural patterns. A candidate hesitating on fundamentals? The AI recalibrates and maps precisely what they know and what they don't.
At the end, you get a structured report. Not a meaningless raw score out of 100. A report with per-skill scores, identified strengths, areas of concern, concrete quotes from the candidate, and suggested follow-up questions for the next interview. Five minutes of reading gives you a clearer technical picture than most human first-round interviews produce.
What changes for each stakeholder
For you, the recruiter
The first technical round no longer depends on a busy engineer. You send the link, the candidate takes the interview whenever they want — evenings, weekends, 6 AM if they prefer. You get the report without involving anyone.
In practice: 25 minutes of AI interview instead of 45 minutes from an engineer. Zero calendar coordination. Zero "they can't do it before Thursday." And every candidate is evaluated against the same criteria, which removes the bias of the engineer who happened to have a rough day.
For the candidate
No more impersonal 90-minute MCQ. The exchange is conversational, adapts to their level, and takes 25 minutes. The AI introduces itself as such from the start — no tricks, no ambiguity.
Candidates I've had test it found the experience more respectful than a standard online assessment. Some told me it was better than most of their human technical interviews. Better because the AI is systematic, patient, and has no ego.
For the technical team
The tech lead only meets pre-screened profiles. And when they do, they have a report telling them exactly what to probe. No more 45-minute exploratory interviews only to discover that the candidate isn't a fit. The final interview is focused, productive, and shorter.
The limits — let's be honest
I'm not going to sell you a perfect tool. AI interviews have limitations, and knowing them helps you use it correctly.
Live coding. The AI evaluates technical reasoning through conversation, but it doesn't replace a pair programming exercise. If you're hiring for a role where real-time coding ability is critical, the AI interview is a first filter, not the only one.
Cheating. Can a candidate use ChatGPT alongside the interview to craft their answers? Fair question. Reqa integrates a three-layer integrity detection system — statistical text analysis, coherence evaluation by a second model, and copy-paste tracking. A candidate who directly pastes ChatGPT output is detectable. A candidate who paraphrases enough to slip through has, by definition, understood the content they're rephrasing. But no system is 100% foolproof, and the integrity report gives you the data to make the call yourself.
Deep soft skills. The AI evaluates technical communication — clarity, reasoning structure, ability to simplify complex topics. But team dynamics, leadership, and real-world conflict resolution are measured face-to-face.
Integrating AI interviews into your hiring process
No need to overhaul your pipeline. The AI interview slots in naturally as a replacement for the first technical round.
Your current process: Resume → ask the engineer → wait → 45-min interview → feedback → call candidate back.
With an AI interview: Resume → send candidate the Reqa link → 25-min AI interview on their own time → report in 5 minutes → you decide independently → targeted final interview with the tech team.
The difference comes down to two things. Time: you go from 5 days to 24-48 hours. Autonomy: you no longer need someone else to make a call on a technical profile.
For interview templates, start with the role you recruit for most often. Define the key competencies, run a few interviews, read the reports, adjust. The first template won't be perfect. The third one will be exactly what you need.
How many good candidates per month?
That's the question I asked myself for five years without putting a number on it. How many solid profiles left because my process was too slow? How many missed placements because the engineer wasn't available the right week?
Every day a strong candidate waits for an answer is a day they can sign elsewhere. In today's tech market, they will.