Last spring I watched a friend demo a real-time AI assistant during a mock sales call. He was mid-pitch, the “prospect” threw an objection about pricing, and a suggested reframe appeared on his screen within about 2 seconds. He glanced at it, adapted it in his own words, and kept going. It looked completely natural. I couldn’t tell where the assist ended and his own thinking began. That blurring is exactly what makes this technology interesting and slightly hard to evaluate.
How the suggestion pipeline works
The mechanics are worth understanding because they explain most of the variation in quality between tools. When you’re on a video call, your computer is already receiving and playing the other party’s audio. A real-time AI assistant intercepts that system audio stream before it reaches your speakers, or captures it from the system output channel, and feeds it to a speech-to-text engine running either locally or over an API.
That transcription gets passed to a language model with some context about your role, the job description, your resume, or whatever you’ve pre-loaded. The model generates a suggestion, and the suggestion appears in an overlay on your screen. The entire loop, from the other person finishing a sentence to a suggestion appearing, runs in 2 to 4 seconds for well-built tools. Some tools are slower. Some are faster. Latency is probably the single most important variable and the hardest to evaluate from a product page.
Browser extensions and desktop applications handle this differently. Browser extensions work inside the meeting tab itself, which means they’re subject to permission changes and updates from Zoom, Google Meet, or Teams. Desktop applications capture at the OS audio layer, which is platform-independent and more stable. Tools like Craqly use the desktop approach specifically because it works across Zoom, Teams, Google Meet, and Webex without requiring any platform-specific plugin or permission grant that could break with an update.
Who this actually helps
I’ll be honest: I think these tools are more useful in some situations than people admit, and less useful in others than the marketing suggests. Here’s my rough breakdown.
Sales professionals doing high-volume outbound calls benefit a lot. They’re handling the same objections repeatedly with slight variations. Having battlecard content or objection-handling frameworks surfaced automatically is genuinely faster than searching a CRM mid-call. Gong’s conversation intelligence research consistently shows that reps who respond to objections within 5 seconds close at higher rates than those who pause longer, and real-time suggestions help close that window.
Non-native English speakers in professional settings also report real value, specifically when they understand a question completely but need a structured phrasing to articulate their answer clearly under time pressure. That’s a language-access issue, not a competence issue, and a tool that helps is doing something defensible.
Where they’re less useful: deeply technical conversations where the AI’s suggestion is generic and the questioner is an expert who’ll notice immediately if your answer lacks specificity. And anywhere the conversation is highly contextual or emotional, like a negotiation or a conflict conversation. The AI doesn’t read the room.
Platform compatibility in practice
Zoom, Google Meet, Microsoft Teams, and Webex are the four platforms that matter for most users. All four work with desktop-based real-time AI assistants because those tools capture system audio independently. The platforms themselves don’t know the capture is happening.
Browser-based tools are trickier. Zoom’s web client and Google Meet occasionally update their audio-handling in ways that break extension-based capture. If you use a browser extension and it stops working after a Meet update, that’s why. It’s not a Craqly-specific issue; it affects any extension-reliant tool.
One practical note: if you’re using dual monitors, put the AI overlay on the second monitor near your camera. That way your eyes move toward the camera when reading suggestions rather than down toward your desk. Small thing, but it looks significantly more natural on video.
The privacy question you should actually ask
Before using any real-time AI assistant, ask the provider three things. Does the tool store audio or transcripts after the session? Is the data used to train or fine-tune models? What encryption is applied during transmission?
The answers vary significantly. Some providers offer explicit no-storage guarantees with documented data-handling policies. Others are ambiguous. A few don’t address it at all in their documentation, which is itself a signal.
Recording consent is a separate issue. Seventeen US states require all-party consent to record a conversation. Whether real-time AI processing counts legally as “recording” is genuinely unsettled. The safe assumption is to treat it the same way: if your jurisdiction requires consent, disclose that AI assistance is in use or consult a lawyer before deploying in professional contexts.
The Bureau of Labor Statistics Occupational Outlook projects strong growth across computer and information technology roles through 2032, and with that growth comes more video-based interviewing and distributed teams. The infrastructure for live AI assistance is going to become more common. Norms around its use haven’t caught up. Right now, the people who use it thoughtfully, understand its limits, and don’t over-rely on it tend to get the most value. The ones who treat it as a crutch usually find out why that’s a problem in the follow-up call when there’s no overlay to lean on.
Setting it up without looking weird
Start with low-stakes calls. Internal meetings, practice interviews with a friend, a call with a recruiter you’re not especially interested in. You need a few sessions to figure out where to position the overlay, how quickly you can glance at it without breaking eye contact, and which types of suggestions are useful versus distracting.
Pre-load context that actually matches the call. For a job interview, upload your resume and the job description. For a sales call, paste the prospect’s company description and your most common objection-handling notes. The quality of suggestions scales directly with the quality of context you provide. Vague context, vague suggestions.
Don’t read suggestions word for word. Use them as structural prompts. The suggestion tells you a direction; your own language takes you there. That gap between the AI’s phrasing and yours is where you sound like a human rather than a teleprompter.