Why this comparison matters

Read.ai is one of the more interesting players in the meeting AI space because it took a clear position: meetings can be measured, and measurement is the path to improvement. It produces meeting reports with engagement scores, sentiment indicators, speaker analytics, and an overall meeting "quality" rating.

Pavleur took the opposite position. We do not score meetings, we do not score people, and we do not produce sentiment analysis. We think those signals are noisy, easily gamed, and culturally corrosive in the kinds of teams we build for. We focus on two things: making the meeting easier while it's happening, and producing a recap that's useful the next day.

This page is a comparison between two products with different philosophies. There is no objectively correct answer — there is a question about which philosophy fits your team.

Feature comparison

Feature	Pavleur	Read.ai
Live transcription	Yes	Yes
Real-time AI assist in-call	Yes — suggestions, recall, definitions	Limited (post-meeting analysis is the focus)
Meeting recap / summary	Yes — engineer-flavored	Yes — general-purpose, well-structured
Engagement / sentiment analytics	No (by design)	Yes — speaker scores, sentiment, talk-time
Meeting quality scoring	No (by design)	Yes — meeting quality reports
Searchable transcript history	Yes	Yes
Email / chat analytics	No	Yes (Read.ai extends beyond meetings)
Zoom / Teams / Meet support	Yes (Zoom, Teams, Google Meet, Webex)	Yes (Zoom, Teams, Meet)
Pricing	$49 / mo (Pro)	Free tier; paid tiers starting around $15 / mo
Privacy posture	Transcripts are the user's data, not scored	Transcripts are scored and analyzed
Org-wide visibility	Per-user	Team / org analytics dashboards

Where Pavleur wins

Live in-call assist, not retrospective analysis

Read.ai's product gravity pulls toward the meeting report — a post-meeting artifact you (or your manager) review. The meeting itself is largely unmediated by AI.

Pavleur's gravity pulls toward the meeting itself. The assist runs during the call: surfacing prior context when something is referenced, defining acronyms quietly, helping recall who said what about which decision last sprint. The point is to make the meeting smoother in real-time, not to grade it afterward.

This isn't a feature gap so much as a different bet on where AI is useful. We bet on "during." Read.ai bets on "after."

Doesn't score people

This is the philosophical disagreement and it matters more than it sounds.

When a tool tells you "Person A had a 73 engagement score and Person B had a 41," you have given your team a metric. Metrics get optimized. People will start performing engagement — talking more, talking when they shouldn't, performatively asking questions — because the metric exists. The metric also will be wrong about quiet thoughtful contributors, because thoughtfulness reads as low-engagement.

We think this is bad for engineering culture specifically, where the strongest contributors are often the quietest. Pavleur deliberately produces no engagement, sentiment, or talk-time scoring. We will not give you a dashboard of which engineer talked most this week, because we don't think that's a question you should be optimizing.

Read.ai disagrees, in good faith, and many teams find the metrics useful. Reasonable people can disagree about this. Just know that if you adopt Read.ai, you're adopting the metrics with it — they're the product's core.

Recap quality for technical meetings

Read.ai's recaps are well-structured general-purpose summaries. They cover what was said, surface action items, and produce a tidy artifact.

Pavleur's recaps lean into how engineers actually think about meetings: decisions made (and by whom), open questions, deferred items, follow-ups with named owners, technical context preserved (not summarized away). When someone re-reads a Pavleur recap a week later, the goal is that they can actually pick up where the meeting left off, not just remember what topic was discussed.

Smaller, focused surface area

Read.ai has expanded into email analytics, chat analytics, and broader productivity scoring. If that's what you want, that's a feature. If you only want "good meeting tooling" and don't want the email-engagement dashboard, Pavleur stays in its lane.

Where Read.ai wins

Meeting analytics is genuinely useful for some teams

If your team or org genuinely benefits from talk-time data — say, a coaching context where a senior leader wants to know whether they're dominating their 1:1s — Read.ai delivers that and Pavleur does not. We think the cases where this is helpful are narrower than Read.ai's marketing implies, but they exist.

For sales teams in particular, where rep-talk-time correlates meaningfully with deal outcomes, the analytics are a legitimate edge. Pavleur is not built for sales calls.

Free tier and lower entry price

Read.ai has a more generous free tier and lower paid tiers than Pavleur. For individual users who want meeting summaries and don't need live assist, Read.ai is meaningfully cheaper.

We're priced where we are because the live assist plus engineer-flavored recap is what we charge for. If you don't need those, you shouldn't pay our price — you should use Read.ai or Otter.

Cross-channel analytics

Read.ai has expanded into measuring email and chat communication, not just meetings. If you want a unified view of how your team communicates across all channels, Read.ai is more or less the only product trying to do that. Pavleur does only meetings, on purpose.

Polished reports for non-engineers

Read.ai's reports look great in a Slack message and read well to non-engineers (execs, HR, ops). If your context involves sharing meeting outputs with people who aren't in the meeting, the reports are well-suited to that.

Pricing comparison

Tier	Pavleur	Read.ai
Free	Limited trial	Generous free tier with feature limits
Individual paid	$49 / mo (Pro)	~$15 / mo (Pro)
Team / org	$49 / seat / mo	Tiered enterprise pricing
Annual	~$348 / yr	Yes, with annual discounts

If you're price-sensitive and the analytics are not a culture-fit issue for you, Read.ai is meaningfully cheaper. If you'd pay more for an engineer-flavored tool that doesn't score humans, Pavleur is what you want.

Who should pick each

Pick Read.ai if:

You want measurable meeting outcomes and your team culture supports metric-driven feedback
You care about cross-channel analytics (email, chat, meetings together)
You're price-sensitive
You're in a sales or customer-facing context where talk-time data correlates with revenue
Your meetings are general-purpose business meetings rather than deep technical syncs
You're comfortable with sentiment / engagement scoring on call participants

Pick Pavleur if:

You believe scoring people in meetings is the wrong abstraction for engineering work
You want the AI to help during the call, not just grade it after
You run deep technical meetings — design reviews, retros, planning sessions
Your recap consumers are mostly engineers, PMs, and EMs
You'd rather pay more for a focused tool than less for a broader one

Verdict

This isn't a comparison where one tool is strictly better. Read.ai is a perfectly good product and the analytics are a real, defensible feature. Pavleur is a perfectly good product and the no-analytics stance is a real, defensible feature.

The question is which one fits your team's culture and the kinds of meetings you actually run. If your meetings would benefit from being measured and your team would react well to the measurement, Read.ai will deliver more value. If your meetings are where decisions get made and your team would react badly to engagement scores on a debate transcript, Pavleur is the safer cultural pick.

If you can't decide, the cheap thing to do is run both for two weeks on the same calendar and see which one you actually open the next morning. That's the only test that matters.