Acedly AI vs Lockera: Which Real-Time Interview AI Is Right for You? (2026)
Acedly AI and Lockera both promise real-time, undetectable AI help during live interviews. This is an honest breakdown of latency, platform coverage, language support, and where each tool wins — written by the team behind Acedly.
Devon Park
Head of Research, Acedly
What Acedly and Lockera actually are
Acedly and Lockera are both real-time AI interview copilots: desktop apps that listen to a live Zoom or Teams call, transcribe the interviewer in real time, draft an answer grounded in your résumé and the job description, and render that answer on a surface the interviewer cannot see when screen sharing. The category is roughly three years old, with Final Round AI the loudest brand on awareness and a crowded long tail of competing copilots. Acedly and Lockera are two of the products candidates evaluate against each other directly.
Both products solve the same problem: the recruiter is on a video call, the candidate's second screen is invisible to them, and a real-time copilot turns that asymmetry into a teleprompter. Both promise sub-second responses, OS-level capture exclusion so the assistant doesn't show up in screen share, and grounding against the candidate's own materials. The marketing pages are interchangeable in many places — and that's exactly why a comparison like this is worth writing.
The differences are in the parts that don't fit on a landing page: how each product measures latency, how many platforms it has actually verified against, how transparent the language coverage is, and how much of the answer-routing logic is a single model versus a routed system. Below, we go through each of those.
Latency: end-to-end vs. model latency
Latency is the single most-quoted number in this category, and the most easily abused. There are two very different numbers a vendor can report:
- Model latency — the time between the assistant's prompt being sent and the first token arriving from the language model. Often quoted as "sub-second" or "under 500 ms." This is mostly a function of which API you call.
- End-to-end latency — the time between when the interviewer finishes the question (last spoken syllable) and when the first word of an answer appears on the candidate's hidden surface. This includes audio capture, streaming speech-to-text, end-of-utterance detection, prompt assembly, model inference, and render.
For a candidate sitting in a real Zoom call, only end-to-end matters. Anything over about 250 ms creates a noticeable silence; anything over 500 ms and the recruiter has visibly moved on.
Acedly publishes a measured ~98 ms median end-to-end latency on consumer hardware, with the methodology written up on the product page (M2 MacBook, fibre Wi-Fi, scripted question set, stopwatch from end-of-utterance to first rendered token). Lockera's public marketing as of mid-2026 emphasises "real-time" and "sub-second" responses but does not, to our knowledge, publish a verified end-to-end median with a stated methodology. That doesn't mean Lockera is slow — it means we can't tell you what it actually is. Stopwatch both products yourself before trusting either one.
Stealth and screen-share verification
Both Acedly and Lockera describe themselves as undetectable on screen sharing, and both almost certainly use the same OS-level mechanisms to get there: NSWindowSharingNone on macOS and SetWindowDisplayAffinity(WDA_EXCLUDEFROMCAPTURE) on Windows. The flag itself is a few lines of code; what matters is whether the product has actually been re-verified after each platform's capture-API updates.
Acedly publishes a per-platform stealth verification table with the most recent re-test date for each of eight platforms, and re-runs the test cadence whenever a major Zoom, Teams, or Webex client update ships. Lockera, based on its public marketing, claims undetectability across major platforms but does not publish a verification cadence or a per-platform date list. That's not a defect of Lockera's product per se — it's a gap in transparency. A capture-exclusion flag that worked on Zoom in January can quietly break in April after a client update; without published re-verification, the candidate has no way to know.
The practical advice is the same as with latency: before any high-stakes interview, do a dry run. Start a Zoom (or Teams or Meet) call with a friend, share your screen the way you would in the real interview, and confirm by hand that the assistant doesn't appear. Do this on whichever product you choose. Trust the dry run, not the marketing page.
Platform coverage matrix
This is where the gap between the two products is the most concrete. Acedly's product page lists eight platforms with a "verified" badge against each. Lockera's marketing is less specific, pitching broad coverage of "all major meeting platforms" without a named list. The table below uses our best read of Lockera's public material as of mid-2026; where Lockera doesn't explicitly name a platform, we mark it as unverified rather than guessing.
| Feature | Acedly | Lockera |
|---|---|---|
| Zoom | Verified, re-tested per release | Claimed (no published verification date) |
| Microsoft Teams | Verified, re-tested per release | Claimed (no published verification date) |
| Google Meet | Verified, re-tested per release | Claimed (no published verification date) |
| Webex | Verified, re-tested per release | Not explicitly named |
| Lark / Feishu | Verified — important for ByteDance and APAC loops | Not explicitly named |
| Amazon Chime | Verified — important for Amazon loops | Not explicitly named |
| Coderpad | Verified, reads editor on-screen | Not explicitly named |
| HackerRank | Verified, reads editor on-screen | Not explicitly named |
| LeetCode (live pair) | Verified | Not explicitly named |
If the bulk of your interviews are on Zoom, Teams, or Meet, both products will likely cover you. If you have an Amazon loop, a ByteDance Feishu round, or a Webex-heavy enterprise interview, Acedly's published verification list is the safer bet. The honest framing: this is a transparency gap, not necessarily a capability gap — Lockera may well work on Webex, but as a candidate you can't easily verify that without testing.
Spoken and programming language coverage
Acedly publishes a named, single-bar coverage list: 30+ spoken languages at the same accuracy bar, with the most-used in interviews being English, Mandarin, Cantonese, Japanese, Korean, Spanish, Portuguese, French, German, Italian, Dutch, Hindi, and Vietnamese. The provider stack — Deepgram, AssemblyAI, Whisper Turbo — is named, and Acedly routes between them per language so the result is consistent. Programming-language coverage runs the same way: 30+ languages at one generation quality, including the dozen interviewers ask for most (Python, JavaScript, TypeScript, Java, C++, Go, Rust, Kotlin, Ruby, SQL, PHP, Scala) and less typical picks like Elixir or OCaml.
Lockera's public material describes multi-language support in general terms but does not, as far as we can tell, publish a named coverage list or provider stack. For interviews in English, this gap probably doesn't matter — both products will likely perform well. For a Mandarin behavioural round, a Japanese system-design call, or a recruiter who code-switches between Spanish and English, the difference between named coverage and general "supports many languages" is the difference between trusting the tool and finding out mid-call.
If you only ever interview in English, treat this section as a tie. If you interview in any non-English language at all, Acedly's published coverage is the more conservative choice.
Multi-model routing vs. single model
A behavioural question, a LeetCode-style coding question, and a senior system-design round are three different problems for a language model. Behavioural answers reward brevity and STAR-style structure; coding answers reward step-by-step reasoning under constraints; system-design answers reward holding a large context window and producing a tree of trade-offs.
Acedly routes between GPT, Claude, Gemini, and DeepSeek based on the question type detected from the transcript. The routing is part of the product, not a setting the candidate has to manage. Lockera's public marketing, as of mid-2026, doesn't make a multi-model routing claim — our read is that it operates against a single primary model, though we'd welcome correction. For most interview rounds, a single competent model is fine. The places where routing matters are the long, complex rounds — the 60-minute system-design loop where the difference between Claude's depth and GPT's structure becomes visible.
This is a feature gap, not a deal-breaker. If your interview pipeline is mostly behavioural and short technical rounds, single-model is sufficient. If you're interviewing for senior engineering or platform roles where the rounds get long and architectural, routing is worth paying for.
Pricing comparison
Pricing in this category shifts often, so we'll describe the shape rather than pin down numbers that may move by the time you read this.
- Acedly runs a flat monthly subscription with no per-token, per-minute, or per-call metering. The price is the price; using it more during interview week doesn't cost more. There is a free starting tier sufficient to test before a real call.
- Lockera has historically positioned itself as the lower-priced option in the category, and that's a legitimate advantage if your job search is short and your usage is concentrated. Tier structures and trial windows have shifted; check the Lockera site for the current state.
The honest framing: if cost is the dominant constraint and your job search is two weeks of intense interviewing, Lockera's lower entry price is a real win. If you're going to use the product over a multi-month search, or if you'd value the transparency/verification on Acedly more than the price gap, the calculation flips.
Onboarding and UX
Lockera generally has a simpler onboarding flow — fewer steps from sign-up to first session, less configuration to set up the résumé and job description grounding. That's a real advantage and we'll acknowledge it: a candidate who installs a copilot the night before an interview wants to be in a session within five minutes, not twenty.
Acedly's onboarding has more steps because there is more to configure: which speech-to-text provider to prefer, which model to default to for which question type, which platforms to enable, how the résumé and JD ground each session. Most of that runs on sensible defaults, but the surface area is larger. The trade-off is straightforward — Acedly assumes you want control over the routing and verification; Lockera assumes you want to start fast. If you don't want either of those choices to be visible, Lockera is the better fit.
Use Acedly if…
- You want a published, verified end-to-end latency number with a stated methodology.
- Your interviews touch Webex, Lark/Feishu, Amazon Chime, Coderpad, or HackerRank — places where Acedly has a named verification and Lockera's coverage is unlisted.
- You interview in a non-English language and want named per-language accuracy tiers, not general claims.
- You're interviewing for senior engineering, platform, or architecture roles where multi-model routing for behavioural / coding / system-design rounds is worth the extra config.
- You value transparency over simplicity — published methodology, published platform list, published model routing.
Use Lockera if…
- You want the simplest possible onboarding and intend to use the product for a short, concentrated interview window.
- Your interviews are almost entirely on Zoom, Teams, or Meet, in English, against fairly standard rounds — the gaps in Lockera's published coverage are unlikely to bite you.
- Cost is the dominant constraint and Lockera's pricing is meaningfully below Acedly's at the tier you'd actually use.
- You don't need or want to think about model routing, language tiers, or per-platform verification — you want a copilot that works on Zoom and gets out of your way.