The AI interview tool market has consolidated around a comfortable but limiting distinction: general video screening platforms (HireVue, Spark Hire, Willo) on one side, and technical coding assessment platforms (Codility, HackerEarth, CoderPad) on the other. The first category doesn't go deep on engineering; the second doesn't conduct real conversations.
Ray, a new AI interviewer from Diyam AI, is a bet that there's a gap between them — and that for technical hiring specifically, that gap matters a great deal. This piece maps how Ray sits relative to the existing field, what it does differently, and which teams it's actually built for.
The Three Tiers of AI Interview Tools (and Where Each Fits)
Before getting to the comparison, it helps to be clear about what problem each category is solving. As we covered in our complete guide to AI screening, "AI interview tool" covers several fundamentally different things:
- General async video screeners (HireVue, Spark Hire, Willo, myInterview) — candidates record video responses to set questions; recruiters review or scores are generated. Good at volume reduction. Not built for technical depth.
- Coding assessment platforms (Codility, HackerEarth, CoderPad, HackerRank) — candidates write or explain code against set problems. Strong signal on implementation ability. Weak at probing thinking, design decisions, or ownership.
- Conversational AI interviewers (InterviewFlowAI, and now Ray) — AI conducts a live adaptive interview, asking follow-up questions based on what the candidate actually says. The signal is qualitative judgment, not just task completion.
Ray sits in the third category, but with a specific focus the others in that tier lack: it's designed from the ground up for technical roles — engineers, architects, and technical leads — not general professional hiring.
What Ray Does
Ray conducts live AI-powered interviews that adapt in real time to candidate responses. Ask a candidate about a system they built, and Ray follows the answer: if they describe the architecture at a surface level, it probes deeper; if they demonstrate genuine ownership, it moves on. The interview isn't a fixed script — it's a structured conversation with dynamic depth calibration.
Key design choices that distinguish it from the field:
- Adaptive follow-up questioning — Ray adjusts the difficulty and direction of each question based on what the candidate actually said, not on pre-set branching logic. This is more similar to a skilled human interviewer than to a scripted chatbot.
- Failure-mode probing — particularly on system design questions, Ray is configured to ask about edge cases, failure scenarios, and trade-offs candidates didn't volunteer. Most candidates prepare the happy path; Ray is explicitly built to surface what lies beyond it.
- Ownership verification — a persistent problem in engineering hiring is candidates describing team-built systems as their own work. Ray's questioning style is designed to distinguish between "I designed this" and "I was part of the team that built this."
- Anti-gaming by design — because the follow-up questions are generated from the candidate's own answers, standard prep guides and LeetCode-style cramming are much less effective than they are against fixed question banks.
Most AI screening tools test whether a candidate can perform. Ray is designed to test whether they actually understand what they claim to have built.
Ray vs the General Video Screeners
Compared to HireVue, Spark Hire, and Willo — tools we covered in our startup AI interview tools roundup — Ray is a fundamentally different intervention in the hiring process.
General video screeners are optimised for volume reduction: moving from 500 applicants to 30 faster than a human recruiter could. Ray isn't trying to do that. It's trying to replace the 45-minute first-round technical interview that a senior engineer currently sits in — and provide a structured, comparable assessment across all candidates who get that far.
The implication is that Ray typically enters the funnel later. You might still use a video screener or resume filter to reach a shortlist; Ray then runs that shortlist through a substantive technical interview without consuming engineering time. For a startup where a senior engineer's time costs $200–300/hour, replacing five first-round interviews per hire with Ray pays for itself quickly.
Ray vs the Coding Assessment Platforms
The comparison with Codility, HackerEarth, and CoderPad is more interesting, because these tools are already specifically targeting technical hiring. The differences are substantial:
| Dimension | Codility / HackerEarth | Ray by Diyam AI |
|---|---|---|
| Format | Code challenges, timed tasks, MCQ | Live conversational AI interview |
| Signal type | Can they implement correctly under time pressure? | Do they deeply understand what they claim to know? |
| Gaming resistance | Anti-cheat tooling (Codility leads here) | Adaptive questions from candidate's own answers — harder to rehearse against |
| Evaluates system design? | Partially (structured MCQ/diagramming in some tiers) | Conversationally, with follow-up depth probing |
| Evaluates ownership claims? | No | Yes — core design intent |
| Startup pricing | Enterprise-only (Codility); limited free tiers | Startup-friendly, per-role model |
| Setup time | Days to weeks (question bank, rubric setup) | Hours — interviews are role-description driven |
Codility and HackerEarth are excellent for testing implementation — whether a candidate can write working code against a defined problem. They're poor at evaluating the things that often matter more for senior hires: architectural judgment, trade-off reasoning, and whether a candidate genuinely led what's on their resume. Ray goes after exactly those signals.
Ray vs Paradox Olivia and Conversational Tools
Paradox Olivia, which we compared to HireVue in our head-to-head, is conversational but operates at the front of the funnel: it qualifies candidates, schedules interviews, and handles communication. It doesn't conduct substantive assessments. Ray is essentially the next step in the funnel — after Olivia routes a candidate to a screening interview, Ray is what runs that interview.
InterviewFlowAI, the per-interview conversational tool we included in our startup roundup, is closer in format but designed for general professional roles rather than technical ones. It doesn't have the domain knowledge to probe system design decisions or follow an answer about database indexing with an intelligent question about read/write trade-offs. Ray's technical depth is its core differentiation over generic conversational interviewers.
Who Ray Is Actually For
Based on how it's designed, Ray is best suited to three specific situations:
1. Startups replacing engineering interview time. If your senior engineers are each sitting in three to five first-round interviews per hire, and you're making ten hires a year, that's hundreds of hours of engineering time going to interviews most of which screen out candidates anyway. Ray is sized and priced to fix this problem specifically — not to fit inside an enterprise procurement process.
2. Teams hiring senior or staff-level engineers. For junior hiring, a coding challenge is often sufficient signal. For senior and staff roles, where the interview needs to evaluate judgment, architecture decisions, and leadership of complex work, a conversational AI with technical depth is a better fit. Ray is explicitly not a tool for volume graduate hiring.
3. Teams with resume inflation concerns. This is a documented and growing problem in engineering hiring — candidates describing systems they observed rather than led. Ray's ownership-verification approach addresses it directly, which neither video screeners nor coding challenges do.
The Honest Caveats
Ray is a newer entrant, which means a few things worth noting. The track record of interview quality at scale isn't yet as established as Codility's or HireVue's. Candidate familiarity with AI-led technical interviews — rather than coding assessments — is still building, which means you'll need to communicate the format clearly upfront. (Candidate transparency matters here for the same reasons we've documented across the wider AI hiring landscape.)
It's also worth being clear that Ray sits at a specific point in the funnel: it's a technical interview tool, not a full ATS replacement. Teams still need resume collection, scheduling, and offer management elsewhere. For startups that already have a lightweight ATS setup, that's fine; for teams looking for a single platform to handle everything, no tool in the conversational AI interviewer category currently does that end-to-end.
The Bottom Line
The AI hiring tool landscape has a real gap between "screens candidates at volume" and "evaluates technical depth." Ray is the most purpose-built attempt we've seen to fill that gap for startup engineering teams — combining live adaptive questioning, system design probing, and ownership verification in a format sized for teams that don't have enterprise procurement budgets or months to configure a platform.
It's worth evaluating alongside whichever video screener or coding assessment tool you currently use — not necessarily instead of them, but for the part of the funnel where neither of those tools gives you the signal you need.
You can learn more and join the waitlist at diyamai.com.
Sharingan AI reviews recruitment technology independently. We have no commercial relationship with Diyam AI — this review reflects our independent assessment of how the product and its approach compare to the existing field.