Conversation intelligence: what the archive actually hears

If your company runs a revenue-intelligence platform — the Gong-and-Chorus class of product — then every client call you take is being recorded, transcribed, analysed and kept. Your voice, at whatever quality your setup delivered that morning, is now a permanent, searchable asset that your manager, your successor and a model will all read from.

This is a landscape page, not a review. We don't rate these platforms, recommend between them, or earn anything from them; several of our readers simply have one switched on whether they chose it or not. What we care about is the narrow question nobody sells an answer to: what do these systems do about the quality of your audio, and what does that mean for how you should sound?

What this category actually is

Revenue-intelligence platforms attach to your calls, capture them, produce a transcript, and then run analysis over the result — talk ratios, topics, competitor mentions, next steps, deal risk. Sales leaders use them for coaching and forecasting. The important structural fact for you is the order of operations: capture first, analyse second. Everything downstream is built on the recording, and the recording is built on whatever came out of your microphone.

The order of operations as this page describes it. Everything below the green band works from what the microphone gave it.

That's different from the note-taking category, which we deliberately don't cover here — different job, different publication. The distinguishing feature of this class is the archive: not "what were the action items", but "what does a year of this person's calls look like".

What their own documentation says about audio quality

We went to the vendors' own pages rather than to review blogs, and read them on 2026-09-20. Two findings, quoted exactly, because the gap between them and what people assume is the whole point of this page.

Read those carefully and a pattern appears. Speaker separation is about telling voices apart. Custom vocabulary is about spelling your product names correctly. Continuous improvement is about the model. None of it is a promise to rescue a noisy microphone — and to be fair to both vendors, none of them claims otherwise. That's an assumption their customers make, not a claim their documentation makes.

We also deliberately don't print the transcription-accuracy percentages that circulate on comparison blogs. We couldn't verify them in vendor documentation, so they don't appear on this site.

Why that gap matters to you specifically

Four consequences, in rising order of how much they should change your behaviour.

• Noise becomes permanent. A live call ends; an archived call doesn't. The traffic under your voice on Tuesday is in the record indefinitely, and will be replayed by people deciding things about you.
• Ambiguity becomes text. When a passage is hard to make out, a transcript doesn't render it as uncertain — it renders it as a confident guess. Whatever reads the transcript later inherits that guess as fact, and nobody goes back to the audio to check.
• Your metrics are computed from the capture. Talk ratios, topic detection and next-step extraction all run on the transcript. A degraded signal doesn't just look bad; it can distort the numbers you're coached on.
• Search only finds what was transcribed. The competitor mentioned in your worst-sounding call may simply not exist as far as the platform is concerned.

The honest framing is this: the archive is a lossy copy of your input, and every layer above it treats that copy as the truth. Clean at the source or don't bother.

What to do about it — in order

The coaching angle nobody enjoys

These platforms were bought to make call review scalable, which means somebody may genuinely listen to your calls. Most professionals find the first review uncomfortable for reasons that have nothing to do with the tool: they've never heard themselves. Get there first. Pull two minutes of your own archived call before anyone else does and grade only the sound — level, floor, tone. The method is on follow-up call habits, and doing it yourself first converts a dreaded review into a thing you already know.

Our position, stated plainly

We link to Gong and Chorus by ZoomInfo directly. We have no affiliate relationship with either — those links pay us nothing, and we're not in a position to tell you which platform to buy or whether to have one at all. That decision usually isn't the caller's anyway. If one is running on your calls, the useful question is what you feed it, and that's a question about your microphone chain, which is our actual subject.

Questions about the archive

Does my company's platform clean up background noise for me?

We found no such claim in either vendor's documentation as of 2026-09-20, and you shouldn't assume it. These systems separate speakers and improve vocabulary handling; that's not the same as removing a leaf blower from your track. Assume the archive keeps what you gave it.

Should I stop caring, since the analysis is mostly about what I say?

The analysis is about what the transcript says you said. Those diverge exactly where the audio was worst, and the divergence is invisible — it looks like confident text either way. That's the argument for input quality in one sentence.

Which of these platforms should we buy?

Not our lane, and we'd be pretending. We cover how you sound on the call; procurement comparisons for revenue-intelligence suites are somebody else's expertise, and we'd rather send you to their own documentation than invent a verdict to fill a page.

Is a recorded call different from a live one, audio-wise?

Same signal, different consequences. Live, a bad passage costs you one moment of confusion. Recorded, it costs you a permanent, searchable, slightly wrong record of the moment. Same fix, higher stakes.

Facts on this page were read on the vendors' own pages on 2026-09-20 and are quoted verbatim. Vendor pages change; if you're relying on a detail here, confirm it at the source.