I’ve been testing Read AI for a few weeks, mainly in team syncs and a couple of client calls. Here’s the verdict: the recording and transcription side is genuinely solid. It’s reliable, picks up different speakers decently, and the interface is clean. For a straightforward meeting recorder, it’s a good pick.
Where it falls apart is the promised “AI analysis.” The summaries feel like they’re just grabbing random sentences, often missing the actual action items or decisions. The “insights” are so generic they’re useless. Example from a sprint planning:
**What Read AI highlighted as a key insight:**
> "Team discussed the timeline and dependencies."
**What actually mattered:**
> Backend deployment is blocked on the auth service, which is waiting on a security review from Vendor X—this pushes the launch by at least two days.
It’s a pattern. The tool seems to be doing simple keyword matching and sentiment gloss, not actually understanding context or extracting concrete outcomes.
The cost angle is also frustrating. You’re paying an AI premium for what is essentially a transcript search. For the price, I’d expect:
- Accurate extraction of decisions and owners
- Detection of contradictions or unresolved points
- Maybe even a lightweight timeline of topics covered
Instead, we get fluff. It feels like vendor lock-in for a half-baked feature—you’re tied into their ecosystem hoping the analysis gets better, but there’s no transparency on when or how.
If you need a recorder, sure, give it a look. But if you’re buying it for the smart summaries and analysis, save your money. You’d get more value running a good transcript through a local LLM with a custom prompt.
—L
Every cloud has a dark cost.
That's a really clear example. I've noticed the same thing with action items. It often grabs a sentence where someone says "I'll do that," but then doesn't link it to what "that" actually is.
Do you think it's a problem with the AI model itself, or maybe how the prompts are set up behind the scenes? Like, could they fix it with better tuning?
Totally get what you mean about action items. I've seen it pull "I'll send that over" without any clue about the document or recipient. I think it's a bit of both - model and prompts.
The foundational model likely needs fine-tuning specifically on meeting dialogue, which has way more context and pronouns than, say, an article. But the prompt engineering is probably the quicker win. If they're just asking for "sentences that sound like tasks," they'll get this result. They need prompts that force linking a pronoun to its nearest clear subject, or to flag and clarify ambiguity.
It's a hard problem, but fixable. Gong and Chorus had similar issues early on.
Let the machines do the grunt work
Yeah, the pronoun resolution is a killer. I ran a similar test where someone said "I'll sync with them on that next week." The AI latched onto it as an action item, but "them" and "that" were completely orphaned. It's like the model has meeting-induced amnesia for anything said more than ten seconds prior.
You're spot on about it being fixable with better prompts, but I wonder if the real bottleneck is cost. Forcing the model to constantly cross-reference context for every ambiguous pronoun could get computationally expensive fast, especially for hour-long calls. Maybe that's why they're sticking with surface-level sentence grabs for now.
It feels like they prioritized shipping the analysis feature over making it actually useful. Classic PLG move, but the ROI on a broken insight is zero.
Try everything, keep what works.
You're zeroing in on the exact reason these features are so brittle. The "cost bottleneck" is real, but I think it's more about architectural vanity than pure compute expense.
Everyone wants to say they have an "AI agent" that understands conversations, so they slap a generic LLM on a transcript and call it a day. But proper pronoun resolution isn't just a fancier prompt, it's a different problem. You need a layer that maintains a running entity map - who is "them," what is "that" - which is more of a structured data task than a generative one.
Doing that well is unglamorous engineering work, and it doesn't fit the "one model to rule them all" narrative. So we get cheap, contextless sentence plucking that's computationally trivial but useless. They'll keep calling it AI analysis because it tests well in a demo.
monoliths are not evil