Every AI meeting notetaker, measured before you buy one.

AI Meeting Notetakers: A Buyer’s Guide for the Mid-Market

Version 2.1.5 · 6 October 2026 · Christophe Rosseel, Machinegeist · machinegeist.ai

Purpose of this document

The market for AI meeting notetakers is crowded, and not everything that is written about it can be trusted. Dozens of tools compete. Some join your call as a visible participant, a bot. Others are botless: they listen from your laptop, and nobody in the meeting sees them. Underneath the feature lists sit harder questions about consent, voice recordings as personal data, and integration of your meeting data in your larger system.

Vendors in this category work hard to shape what you read. We are opinionated and independent. Machinegeist has no commercial relationship with any vendor here; nobody paid to be included and we get no commission on the links in this guide. This guide covers 14 commercial tools, and the options you are likely already paying for. We also cover the legal position and we look at what users say (after having screened reviews for independence first).

The verdict

Everything that matters is in the section below. The rest of the guide is the evidence. Read on if your situation needs more detail.

What we would buy

What we would not buy

Best for Dutch: Fireflies, provisionally. Otter and Google’s notes feature do no Dutch at all. The one real test, a Belgian consultant running Fathom and Fireflies side by side for six months, ended with him switching to Fireflies because of Dutch, and months of our own use at the product studio Voxdale agree. Hold that lightly: these tools buy their speech recognition from a handful of engines underneath and can swap them without telling you, so any ranking ages in months. The same test measured raw Dutch transcripts at 75 to 80 percent correct while the summaries on top read 90 to 95, so every tool demos better than it transcribes. Standard Dutch will mostly solve itself as the engines improve. Flemish, dialect and sentences mixing Dutch, French and English will not. Test one real Dutch meeting during the trial, raw transcript against summary, before you sign.

Check what the export contains. The deepest difference between these tools is invisible in a demo: whether an exported meeting ties each line to a person your other systems can recognise. Four manage it: Google’s transcript feature, Avoma, Leexi, and usually Fathom. The rest hand you names as plain text, and matching them to your CRM becomes your job, at your cost. Circleback shows how little a demo tells you. It identifies speakers better than most, holds an email address and a lasting identifier for every attendee, then labels each transcript line with a bare name and leaves you to join the two yourself. No tool understands company groups either: KBC and CBC are one banking group, and every tool here files them apart. When Dutch and the export both matter to you, Dutch wins: take Fireflies and price the name-matching by hand. A transcript your systems can join is worth little when one word in five is wrong.

Circleback, if you arrived here because of it. It is the tool being recommended hardest this autumn, and on a video call in English it deserves some of that. For a Benelux company it carries four facts to weigh before the free trial: every recording is processed and held in the United States, at every tier; it saves a person’s voice the moment you type their name, and never asks them; it picks one language per meeting and transcribes everything in it, so a Dutch meeting with French passages loses the French; and not a single independent report of its Dutch exists yet. We do not recommend it for multilingual Benelux meetings today. We have not put it on the list above either, because everything we know about it comes from its own documentation, and that documentation is unusually clear.

Bot or botless, and the squeeze. A bot can be seen, managed and increasingly blocked; a botless tool records unseen from your laptop, and the whole consent question lands on your people. The platforms are squeezing bots. Google now holds unverified joiners in a default-deny queue, Microsoft sorts them into a labelled lobby and rolls out an optional outright block during October 2026, and Zoom has pushed recording into its own servers, replacing the bot with a banner on screen. If your consent process relies on people noticing the bot, it is aging fast.

The legal position, short. Recording voices needs a legal basis under the GDPR, and a visible bot nobody objects to is not consent. Naming speakers by voice creates biometric data and needs written permission. Reading staff emotions with AI has been banned in the EU since February 2025, and AI transparency duties have been live since August 2026. Otter, Fireflies and Granola are each defending US privacy class actions. Write your recording policy before you buy anything.

On price, work out the total. Advertised prices mislead in this category: Fireflies tops up AI credits automatically, Avoma’s sales bundle lands at $87 to $97 against an advertised $19, and the recorder devices add hardware and caps. Check what you already pay for first, since Teams Premium and Google Workspace include basic notes. Then start with the ten or twenty people who meet clients.

There is an evaluation checklist near the end. If you run only one test, make it a real Dutch meeting, exported raw.

Start here: do you need a third-party tool?

Microsoft Teams and Copilot

What you get. Recordings turned into text, AI summaries, action items, speaker names, and a recap with chapters and a timeline of who spoke when. Notes flow into Planner, OneNote, Outlook and SharePoint.

What it costs.

Both are paid extras. No standard Microsoft licence includes meeting AI. A company already paying $39 per person for E3 will pay $69 in total once it adds Copilot.

Where it falls short. It only works in Teams. The summaries are decent and rarely as good as a specialist tool. To connect it to a CRM other than Microsoft’s own, you need yet another licence. It handles about fourteen languages, and the AI answers work best in English, so test Dutch on your own account before you rely on it.

What IT departments report. Transcripts label everyone “Speaker 1” until each person finds and turns on a setting. Meeting rooms stay unreliable even when configured: administrators report voice training and correct policy working with one brand of ceiling microphone and failing with others, and Microsoft’s own pages disagree on how many trained voices the feature supports. Room recognition only works inside a single company, which rules it out for client meetings.

Best for. Companies working entirely inside Microsoft, with no meetings on other platforms. Teams Premium is the sensible place to start.

Google Meet and Gemini

What you get. A feature called “Take notes for me” writes a transcript, a summary and a list of actions, and saves them to Google Drive. It is included on Business Standard at $14 per person per month and above. The $7 Business Starter plan does not include it.

Where it falls short. It only works in Google Meet. It handles eight languages, and Dutch is not one of them. It handles one language per meeting, so a meeting that moves between languages will not work. The notes go to the meeting organiser’s Drive, and it is the organiser, not the attendee, who needs the right licence. You cannot search across past meetings, and it does not connect to a CRM.

Something to act on now. On 16 July 2026 Google added new settings for this feature, including one that starts taking notes automatically in any meeting with three or more guests. That setting is switched on by default for Business Standard and Business Plus, and it has applied to users since 29 September 2026 unless an administrator changed it. If you run Google Workspace, this has arrived whether you asked for it or not.

The reason to look at Google anyway. Google’s transcript feature, which is separate from the Gemini notes, tags every line with the speaker’s ID from your own staff directory. That ID is the same ID your other Google systems already use, and it is still the same person next month. Nothing else in this guide manages that. If you want meeting data that other systems can act on, this is the reason, and you have to switch it on separately.

Best for. Google Workspace teams who want basic notes at no extra cost, or technical teams who want transcripts their other systems can use.

Notion AI Meeting Notes

What you get. A meeting-notes block on any Notion page. Nothing joins your call. The desktop app listens to your computer’s sound and your microphone, so it captures Zoom, Meet and Teams alike. It handles Dutch and French among others, writes summaries with action items, and the transcripts can be searched next to the documents they relate to.

What it costs. It is included in Notion Business at $20 per person per month, and in Enterprise. If you already pay for Notion Business, it costs nothing more.

Where it falls short. It records at most ten hours per person per day. Speaker names come from the calendar invitation rather than from the voices. Notion itself lists three cases where this fails: several people sharing one microphone, group meetings, and using it in a browser or on a phone. Speaker names only work in English, so a Dutch meeting is transcribed and nobody is named. Two different Notion pages disagree about how many languages it supports.

Two warnings before you roll it out. The message that tells people they are being recorded plays through your computer speakers rather than into the call, so anyone wearing headphones announces it to nobody. And the Notion desktop app works out that you are in a meeting by watching whether your microphone is in use and by inspecting your network traffic. That is switched on by default rather than something you opt into. Notion engineers defended it on a technical forum under their own names, and the objection stands: it is on unless you turn it off.

Best for. Teams already on Notion Business who want notes plus a searchable archive without paying another supplier, and who do not need speaker names outside English.

Where the built-in options stop

The tools bundled with your existing software answer the question “what did we discuss”. They do not answer “what has this client told us across six meetings, and what did we promise them”. Notion is the partial exception, because its transcripts sit in a searchable workspace.

The security argument for staying with what you own. Your recordings never leave your existing supplier. No third company touches them. For regulated work that counts. It also concentrates the risk in one place. Two separate switches control whether Microsoft will hand your transcripts to another system, and turning off one of them strips every speaker name out of the data, with nothing to warn you.

Bot or botless

Bots (Fireflies, Otter, tl;dv, Avoma, Leexi, plus Circleback and Fathom unless you change a setting) join the meeting as a participant that everyone can see. They record from the meeting service itself.

Botless tools (Granola, Krisp, Plaud, Pocket, Notion, and the free open-source apps) record from your own computer or from a device on the table. Nobody sees anything.

The recording route has its own section below, because it decides how well a tool can work out who spoke. A bot usually receives each person’s voice on its own separate channel, which makes working out who spoke almost free. A botless tool hears everyone mixed together and has to guess.

The platforms are squeezing these tools, and each does it differently now. In March 2026 Google added a second admit queue for join requests it cannot verify, the ones a host “is more likely to need a closer look” at, in Google’s words. The default answer for that queue is deny, and notetaker bots land in it, which the vendors’ own help pages confirm. Google has published one sanctioned route, the Meet Media API, which takes the meeting audio from Google’s side with the host’s consent, and it is open to developer-preview testers only. So a production tool still has no approval path. Microsoft’s equivalent is still rolling out, with completion now promised by the end of October 2026, later than most reports claimed. Its lobby sorts detected bots into “Waiting” and “Suspected threats”, and a registration programme lets a vendor mark its bot so Teams recognises it. Two limits: registration is a preview open to a handful of suppliers so far, and a recognised bot still needs the organiser’s approval to get in. The admin policy has two positions, do not detect, or require approval when detected. A third position that blocks every detected bot outright reached early-release tenants in September and reaches everyone during October 2026, switched off until an administrator turns it on.

Zoom is the interesting one, and our earlier description of it was too generous. Zoom’s rules reserve the ordinary route for humans and push notetakers towards a different one. Since March 2026, a bot joining someone else’s Zoom meeting has to be tied to a named person who is present. It stops the moment that person leaves, so it cannot record on its own. The route Zoom now recommends instead takes the audio from Zoom’s own servers, and nothing joins the meeting at all. So Google and Microsoft are making bots easier to see, while Zoom has moved the recording out of sight: no participant and no waiting room. The host keeps controls, different ones. A banner tells the room the meeting is being shared with an app, a notifier names the app and the person who connected it, and the host can require approval per app or block the route entirely. If your consent process depends on people noticing the bot, test whether anyone notices the banner.

Some large buyers have already decided. Two universities publish policies that block third-party notetakers by name, listing Fireflies, Otter, Read AI and MeetGeek. Both tell staff to use whatever is already built in. Their reason is that these tools keep recordings, transcripts and attendee details outside systems the university controls.

The free, open-source option. Meetily is the best known, with more than 31,000 stars on GitHub. It runs on your own machine and you supply your own AI for the summaries. We looked at it closely and cannot recommend it for real work. Its own bug tracker shows it throwing speech away with no error: non-native English speakers get detected as another language and mostly discarded, speech in another language is replaced with English sentences nobody said, and short words like “OK” and “No” were scored as noise and dropped until that bug was closed as fixed on 12 September 2026. A new release shipped on 10 September 2026, 201 issues are open against 139 closed, and the paid version is built from different code than the free one, so inspecting the free version tells you nothing about what you would pay for. Its compliance claims, “GDPR, HIPAA and SOC2 by design”, name no auditor, report or date. One correction to something we published before: the widely repeated report that Meetily fails on longer recordings was withdrawn by the person who reported it, and reinstalling fixed his case. The faults above are worse than the one that was withdrawn, and its own bug tracker documents every one of them.

How many people are already doing this. Three quarters of professionals say they use an AI notetaker at work, according to a survey run by one of the vendors. Even heavily discounted, that means people in your company are recording today, before anyone decided who may use one, where the recordings end up, and who asks permission.

Working out who said what

Every vendor claims their tool tells you who said what. Almost none explain how, and the differences decide whether those notes are any use outside the tool.

Three questions hide inside “who said that”, and the tools answer them at different levels.

Telling the voices apart, which vendors call diarization. The tool separates the audio into different speakers. You get “Speaker 1” and “Speaker 2”.

Putting a name to each voice, which vendors call speaker identification. You get “Jan Peeters”.

Matching that name to a record you already hold, which is entity resolution. You get the right contact in your CRM, filed against the right company, in the right corporate group. That last step is where the value is, and no notetaker does it. The question when buying is whether the tool makes it possible at all.

The first two get merged constantly, including by the people who build these tools. They are separate problems with separate costs. Diarization is close to free on a video call, where each person arrives on their own audio channel, and unsolved around a table, where one microphone hears everyone mixed together and the tool has to guess. That is why the same product can look excellent on a call and credit one person with the whole meeting in a room. Identification is a separate step again: it needs a reference for each voice, or a name from the invitation, on top of whatever the diarization produced. The third question decides whether any other system can use your notes.

How each tool decides who is speaking

What you can get out of each tool

This is the table to take to a sales call. The question it answers: if you export a meeting, can any other system work out who said what?

Four of these need a closer look.

Fireflies looks like it works and does not. Its data includes a speaker number on every line, and attendee email addresses on the same record. Anyone reading that will assume the two connect. Fireflies’ own description says the number only means something inside that single transcript, and nothing on the attendee list refers back to it. This is an expensive misunderstanding, because you only discover it after you have built something on top.

Circleback comes closest. This is the tool the market currently holds up as the best in the category at knowing who spoke, and it earns part of that. Every attendee arrives carrying a job title, a company name, an email address and a lasting identifier, and action items are assigned against that same identifier. Then the transcript labels each line with a bare name, and so do the insights it extracts. The record you need sits in the same response, one field away, unreferenced. Ask for it in writing before you sign, because no competitor puts it there either.

Owning the staff directory is not enough. Microsoft knows every employee and every team in your company, and hands you a name as plain text with the email addresses on a separate page. Google, on one of its two note-taking products, hands you a proper ID on every line. Both companies could do either. Microsoft designed its transcript for a person to read, and Google designed this one for a computer to use.

Otter is the exception. It and Circleback are the two tools that recognise the same person from one meeting to the next, both through stored voice profiles. Otter goes further: its profiles are shared across the company, and it can go back and correct old transcripts once it learns a voice. It is also the only tool whose transcript is one unbroken block of text with no structure at all. It remembers people better than anything else here, and its export is the hardest to use.

In-person meetings

If your meetings happen around a table, read this before anything else.

• Fathom on a laptop cannot tell people in a room apart, and puts your name against everything anyone said, in both the transcript and the summary. Its iPhone app, released in October 2026, records in-person meetings; Fathom publishes nothing on how it separates speakers there.
• Avoma names the meeting host as the speaker for the entire meeting.
• Granola cannot separate people sharing one device on a laptop. Its phone app does separate the voices, and labels them “Speaker A” and “Speaker B” without naming anyone.
• Circleback records a room through its phone app, and publishes nothing about how well it tells people apart in one.
• Microsoft Teams, without trained voices, credits the room instead of a person.
• Read AI, when recording locally, produces a transcript with no names in it at all.
• Notion lists shared microphones, group meetings and phone use as the cases where names fail, and recommends phones for in-person meetings.
• Plaud was tested with seven people in a noisy room. Short interruptions were all credited to one person, and two of the seven were mixed up with each other. Plaud’s own support pages admit that background noise and people talking over each other can merge two speakers into one.
• Pocket was tested with three people at a table over four hours. The user measured the transcript at about a third correct, and everyone came out as “Speaker 1” even though each person had said their name aloud. Another user recorded two people and got five different speaker labels, with some passages credited to both of them at once.

No error appears when this happens. You get a clean, confident transcript in which one person appears to have said everything. If that feeds your CRM, you have a confident and wrong record.

One more detail for anyone in sales. On Teams you can only correct your own name. Microsoft’s documentation says plainly that you cannot fix anyone else’s. The customer’s name stays wrong.

Making the notes worth something afterwards

A thousand transcripts becomes an asset when a line of speech can be traced to a person, that person to a company, and that company to the group it belongs to.

A Belgian example makes the gap obvious. KBC and CBC are one banking group, CBC being the French-speaking side. Search a year of transcripts for KBC and every CBC meeting stays hidden. Ask which conversations involved the group and you get a wrong answer with nothing warning you. The same thing happens with Crop’s NV, where two registered companies of 12.3 and 169.2 million euro sit inside a group turning over roughly 660 million. We did it to ourselves. Our own system recorded TriFinance as a Dutch company of 161 people. TriFinance is a Belgian group of roughly a thousand, spread over four countries.

Not one tool in this guide says anything about parent companies or subsidiaries. Every one of them matches on a single email address or a single website address. Fathom comes closest: it looks at everyone’s email addresses and picks whichever company appears most often. Its own documentation admits this gets the answer wrong when several outside companies are on the call, and there is no way to correct it. Put two companies from the same group on a call and it files the meeting under whichever one brought more people.

Sorting out which record is which was never the notetaker’s job. It falls to you, and the tool you pick decides how much it will cost.

Six rules, worth applying to any system that will consume your meeting notes.

Never match two records by name alone. Match on a proper ID first, then a website address; use a name to raise doubt, never to settle a match. Matching by name is how a profile reading “Co-Founder Codify AG” entered our own systems as a Singaporean education-finance company. We fixed it by letting the system refuse to answer; nineteen percent of the profiles it reads now deliberately produce no employer.

A website address does not identify a company. Parent companies, shared websites and agencies legitimately share one address. When two records collide on it, that is telling you something about how the group is structured. A person needs to look at it.

One company, one record. Country and branch pages attach to the group rather than becoming new companies. Otherwise you count one customer three times.

A person decides when two records are merged, never the software. Someone names which record survives. Write down what happened before deleting anything, because that note is the only remaining evidence that the other record ever existed.

Never rewrite history, with one exception. When two records merge, the meetings and notes attached to both must survive. So the only thing you may ever change on a past event is which record it belongs to.

When someone changes job, add a link. Do not move them. The history stays with the old company and a new connection is made. A sentence in a transcript saying your main contact is leaving is something your CRM should know the same evening, and no tool in this guide will tell it.

Being straight about where we stand. We build this layer for clients. We have tested our own merging tools against test data and against thirteen duplicates we sorted out by hand. We have not run them at scale, and we cannot merge duplicate people at all yet.

What to ask a vendor. Ask to see the raw data from a real meeting. Vendors demo the tidy report. Four questions answer everything. Does each line carry an ID, or only a name? Is that ID still the same person next week? When the meeting ends, does the tool send you who was there, or do you have to ask it separately? And what does it match on when it writes to your CRM? If the answer is a name in plain text, the work of sorting out who is who belongs to you, and you should price it before you sign.

Comparing the tools

Prices

Per person per month, paying yearly.

What the advertised prices hide.

• Fireflies signs you up for extra AI credits automatically when you run out. Its own help pages say so. The credits included with your plan arrive once when you join, not every month, and each request costs a different amount depending on how much work it takes. What you pay comes out of that combination rather than the $19 headline.
• Avoma charges its sales features per person on top of the basic price. A sales person with both add-ons costs $87 per month on the middle plan, or $97 on the top one.
• Plaud needs a device costing $159 to $189, and unused minutes do not carry over. Only the Team plan lets you control which country your recordings are held in, whatever the badges on the website say. If you connect it to an AI assistant through its newer connectors, Plaud’s own documentation says your recording data passes through a server in the United States, though it is not kept there afterwards.
• Pocket needs a device at $139 on offer against a $199 list price. The free plan records without limit and names nobody; naming people costs $29.99 a month, or $19.99 paid yearly. Its own website gives two different retention answers on the same page, and no price in euro appears anywhere.
• tl;dv’s free version is capped for the life of the account rather than monthly. Your first ten meetings get full notes. After that you get the first ten minutes of each meeting, permanently. Its pricing page now quotes euro only.
• Leexi’s cheapest-plan hour limit appears only in articles written by its competitors and on no Leexi page we could find.
• Krisp reserves single sign-on and automatic account removal for its top tier, so a mid-market buyer on the cheaper plans has neither.
• Fyxer meters every plan in credits, and is not a serious meeting tool on its own.
• Circleback is the exception on this list. We found no credit system, no overage and no per-seat add-on. What the cheapest paid plan buys is history, since the free plan keeps 30 days and then drops what came before.

What 50 people costs per year

Most companies do not need everyone on a paid plan. Sales people, project leads and directors get the most out of it. Start with ten to twenty and add more once you can see it working.

Features

Working in Dutch and French

For a Benelux company, language rules tools out faster than anything else.

Otter does not support Dutch. That removes it wherever Dutch is spoken, before you consider anything else.

Circleback claims more than 150 languages and decides once. Its documentation says it detects the language spoken most and transcribes the entire meeting in that one. In a Brussels meeting running in Dutch with French passages, the French passages come out as Dutch. Nothing in its documentation covers a meeting that switches.

On everything else, be sceptical of what you read, including what we have published ourselves. Two claims circulate widely about tl;dv: that it struggles with accents, and that it mixes up speakers. We repeated both. Both trace back to articles written by its competitors and to review-site figures quoted second-hand, so we withdraw them rather than pass them on. A “90 to 95 percent accuracy in Dutch” figure for Fireflies is the same shape. It comes from a site earning commission, with no test, no sample and no date, and we had repeated that too.

One Dutch source survived the screening. Frederik Vincx of Enter Digital, a cooperative near Ghent, used Fathom and Fireflies side by side for six months across real meetings and wrote it up in Dutch. He found the raw Dutch transcript was roughly 75 to 80 percent correct, while the summary built from it read 90 to 95 percent right. One client’s first name was swapped for a different name, and nothing flagged it. Dates and times were correct throughout. He explains why: people swallow syllables, speak dialect, and mix Dutch, English and French inside a single sentence. He moved from Fathom to Fireflies because of Dutch specifically. He gave up on in-person recording after trying several microphones and laptops. His conclusion was that you cannot send these notes out automatically, because a person has to read them first.

Look at the gap rather than either number. The summary reads better than the transcript underneath it, which is why every tool demos well.

Flemish in particular. Speech recognition is measurably worse at Flemish than at Dutch from the Netherlands, because these systems learn mostly from the latter. French words that are normal in Belgian Dutch get written down wrong. Informal speech and dialect do much worse again. Sound quality matters more than accent: a clear Flemish recording beats a noisy Dutch one. No tool has solved this, and no vendor publishes any Flemish test results.

So which one is best for Dutch?

The elimination round is easy. Otter and Google’s notes feature do no Dutch. Notion transcribes it but names no speakers. Meetily replaces it with English sentences nobody said. Most of the rest claim Dutch support, and across all of them we found exactly one usable ranking signal: Vincx’s six months of side-by-side use, which ended with him moving from Fathom to Fireflies because of Dutch. So Fireflies starts as the favourite, on the strength of one careful tester comparing two tools. Our own experience points the same way: at Voxdale, the hardware product studio where we work half the week, Dutch meetings run through Fireflies and the transcripts hold up. Weigh that for what it is, field use rather than a controlled test, and by people who had already read the evidence above. No test we could find covers the rest.

That answer is thin, and there is a structural reason it will stay thin: the notetakers do not do the listening themselves. Speech-to-text comes from a small group of engines one layer down. Granola and Avoma both name the American supplier Deepgram among their transcription providers. tl;dv names AssemblyAI and ElevenLabs, Leexi names Gladia and Microsoft. Otter and Krisp say they built their own. Meetily bundles the freely published Whisper and Parakeet models. Fireflies names AssemblyAI and Soniox, on a trust page that blocks automated reading and opens in any browser. Fathom’s equivalent page lists seven AI suppliers that handle meeting content and never says which one turns speech into text. So two tools can share one engine without either telling you, a difference you measure today can be a settings difference rather than a capability difference, and a vendor can swap engines without a word to its customers. The engines themselves change hands: Gladia went to OVH in July. Any Dutch ranking describes one moment in someone else’s supply chain.

The engine layer is also where the good news sits. These engines are multilingual models, and each generation handles Dutch measurably better. OpenAI publishes error rates per language for its Whisper models: on the standard benchmark, Dutch errors fell from 9.9 to 6.7 percent across two model generations, ninth-best of roughly a hundred languages, and OpenAI claims a further ten to twenty percent reduction for the generation after. Note what that benchmark measures: native speakers reading Wikipedia sentences aloud. That is how seven percent errors on the benchmark and Vincx’s twenty-plus percent in real meetings are both true. The benchmark side of that gap keeps closing underneath every tool on this list whether its vendor does anything or not, and we expect clean, standard Dutch to be a solved problem within a few product cycles. What we do not expect to solve itself: Flemish, dialect, and sentences that mix Dutch, French and English, because the models barely see any of the three in training and no vendor publishes test results on them.

So buy accordingly. Choose on the properties that hold still: what the export contains, how you leave, where the recordings sit. Treat Dutch accuracy as a moving number. Test it at the trial, with the checklist near the end, and test it again at renewal, because the engine underneath may have changed twice in between.

What users report

What survived our checks, tool by tool. Where nothing survived, we say so.

Fireflies. The billing model is the main finding, and it comes from Fireflies’ own help pages rather than from reviews. It signs you up for more AI credits when you run out, the credits included with your plan arrive once rather than monthly, and each request costs a different amount. Reviewers describe the trap that follows: the account is tied to a work email address, you have to log in to contact support, and the billing continues. People you send notes to see only a few bullet points unless they create their own account, which matters if your clients are the recipients. We withdraw a claim we had published, that the tool keeps joining meetings after you cancel; what is real is that it joins meetings you were invited to but did not organise. From the review sites, nothing survived on how it handles Dutch, Flemish or French; the Dutch evidence that does exist is in the language section.

Granola. Recordings failing without warning is real, specific and recent: a user on the July version lost a full day of transcripts to an unreliable internet connection and only found out afterwards, and Granola keeps no audio, so there was nothing to recover. A correction we owe, having said otherwise ourselves: Granola does let you download everything. The real limits are sharper: one download per account per day, no administrator export, and the feature arrives switched off on the top tier, which is the tier most likely to need it. At least four people have built their own download tools against Granola’s private API. Note folders were shared with entire companies by default, reported first on a technical forum and later by The Verge.

Fathom. The Dutch findings above are the core of the evidence on it. A claim we and others repeated does not survive: that Fathom needs you to be the meeting host on Teams. It traces to a single competitor article citing nothing, and Fathom’s own documentation lists different requirements; the real problem is getting admitted to meetings you did not organise. Two practical traps: a personal email address used to sign up is burned forever if no meeting follows within seven days, and there is no way to download everything at once, which makes leaving expensive. We found no billing or refund complaints at all, which is worth knowing in a category full of them. Superhuman, the company formerly called Grammarly, bought Fathom on 14 September 2026; Fathom says nothing changes for users today, so ask what changes at your renewal.

Otter. All the evidence that survived our checks is about control. Three separate IT sources describe the same cycle: someone sends you notes, you make an account to read them, signing in with Microsoft or Google gives it your calendar and contacts, and it then joins everything with a meeting link and emails the notes to everyone, repeating the cycle. One IT forum describes the case that should settle this for most readers: a guest invited briefly, removed so the room could discuss a salary, and sent the transcript anyway. A named Microsoft specialist explains why blocking it fails: it runs from the account of whoever invited it and arrives as a guest, so blocking it inside your own company never reaches it.

tl;dv. On 4 August 2026 a researcher published a fault in its database that reportedly let any signed-in user list 181,874 meetings belonging to other customers, and possibly join calls in progress. He says he told the company in January and that it was still open when he published; a security publication reported the same thing the same day. The company replied on its blog the next day, revising the post over the following week: it counts two separate faults rather than one left open for six months, admits that the second, which it calls recent and closed within a day, let an outsider into a few live meetings when an organiser admitted them, says no recordings, transcripts, notes or billing data were reachable, cites its penetration-testing vendor for the first fix, and is removing the database technology involved altogether. The researcher has not accepted that account, and both versions agree on the part that matters for a buyer: an outside report from January went unanswered for six months by a vendor whose security page promises a response within 24 hours. Separately: its promise to keep data in Europe is a setting on your account rather than a property of the product, with recordings stored in Germany and transcript processing in Europe or the United States depending on that setting. We looked for evidence about accents and mixed-up speakers and found none that survives checking.

Krisp. The most important finding comes from Krisp’s own documentation. Removing background noise happens on your computer, but turning speech into text may go to Krisp’s servers depending on language and settings, recordings are kept in the cloud, and fully local processing is reserved for the top tier. So nothing joins your meeting, and your audio still leaves your computer. Yearly renewals with no warning and no receipt are well supported across two sites and ten months; “never gives refunds” is too strong, and we had repeated it, since refunds follow disputes rather than requests. One structural warning: Krisp sells noise removal, accent softening and note-taking on one subscription, so no overall score anywhere tells you anything about the notes.

Read AI. Four universities publish warnings about it, though two share wording and count as one. They agree that it joins meetings and writes them up without the host or the other people knowing, and one describes clicking a link to read someone else’s notes creating an account that then joins your meetings. Read AI’s own help pages confirm it now joins unscheduled meetings on Google Meet and Zoom. On its Google integration, people outside the host’s company are not listed as seeing any permission box, which matters if you record client meetings.

Avoma. Whether it gets speakers right is disputed, and we publish the disagreement: two reviewers with stated job titles list it as a weakness, a third lists correct names and correct CRM filing as strengths. One practical trap: recording fails if Zoom’s own recording is running and the Zoom account has run out of storage.

Plaud. A dated test with seven people found short interruptions all credited to one person and two of the seven mixed up, with the reviewer correcting names by hand afterwards; the summary built from that faulty transcript read error-free, the same pattern Vincx found in Dutch. Plaud’s own documentation admits that noise and crosstalk can merge two speakers.

Pocket. The newest product here and the one moving fastest: $11 million raised in June 2026, more than 130,000 devices sold, and a good hardware idea, a card-sized recorder that clips to your phone. Its 4.8 rating comes from a review funnel the vendor controls, and the complaints that do exist cluster on three things a consultancy hits constantly: several people in a room, languages other than English, and getting your money back.

It decides on its own that you are speaking a different language, tells you nothing, and a regional accent is enough to trigger it. A user in Wales recorded English meetings with the app set to English and got Welsh transcripts, confirmed through the API; two more users reported the same, and none of the three threads shows a fix. A user in France reports that French is not reliably detected and puts it plainly: if the language identification is wrong, everything built on top of it is wrong. Pocket claims 120 languages, publishes no list, and says nothing about accents or mixed-language meetings.

Five separate users report the device stopping mid-recording and losing everything captured to that point. And in January 2026 Pocket put summaries users had already created behind a paywall and cut the free plan’s history without warning, then reversed it within 48 hours and refunded those who had upgraded in panic, which is to its credit; what the incident showed still stands, since your existing notes can go behind a paywall overnight.

Pocket runs an affiliate programme paying 6 to 20 percent commission. Assume any “Pocket review” you find is earning money from your click until you have checked otherwise.

Leexi. We looked at this closely because it is the only vendor here based in the Benelux, and the result is less flattering than its reputation, which we had helped build. Leexi’s own privacy policy says the data sits on Amazon servers in Europe and names Pipedrive and Google as suppliers subject to US law; it does not mention France, which its Dutch marketing page claims. The speech-to-text is handled by Gladia and Microsoft, hosted in Europe, neither of them European companies, though Gladia’s purchase by the French company OVH on 31 July 2026 helps the European argument. Its ISO 27001 claim names no auditor, certificate number, scope or date. Leexi pays commission through a Belgian telecoms company, so Belgian recommendations need checking for a commercial connection before they count. Leexi may still be a fine tool. The sovereignty story is weaker than it sounds, and we found no solid evidence in any language about how well it handles Dutch or Flemish.

Circleback. Nothing survived our checks. It is one of the newest tools here and the one being recommended hardest right now, which is the point at which independent evidence is thinnest. It pays 30 percent commission per referral, the highest rate in this guide. Our first search for its name returned four affiliate sites and a competing transcription vendor. The endorsement that put it in front of this category in August 2026 came from an investor in the company, which several people in the same thread pointed out. We found no independent account of its Dutch, no test in a room, and nothing from an administrator. Everything we say about it above comes from its own documentation, read on 1 September 2026.

Fyxer. People who already owned a notetaker described its version as unnecessary rather than bad, which is the more useful verdict. Complaints about cancelling recur, including charges of $450 and $900 after people believed they had cancelled. Every review venue we found for it was paid, self-quoting or defunct, so we treat all of it as reported rather than established.

Security and compliance

Ask for the certificate itself. Two vendors here claim certifications with no auditor, no number, no scope and no date attached. One claims to be “compliant by design”, which describes how it was built rather than anything anyone checked. One publishes nothing at all: no certification, no security page, no list of the suppliers it passes your recordings to, and no way to keep them in Europe.

Where your data sits is a question about the whole chain. Ask whether it covers the recordings, the transcripts, the summaries and the AI processing. At least one vendor keeps the recordings in Europe while sending the AI work wherever the account is set to.

The legal position

Consent across Europe

The GDPR requires a legal basis for every person whose voice you record. In practice that means clear permission, or a written assessment of why you may proceed without it. Nobody objecting when a bot appears is not permission. Fines reach 20 million euro or 4 percent of worldwide turnover.

Belgium. You need clear permission from everyone, and you must explain why you are recording and how long you will keep it. Individuals may record their own conversations. Companies are held to a higher standard.

Netherlands. Much the same. Where employees are being recorded, the works council must agree.

Germany. Recording without permission can be a criminal offence. This is the strictest country covered here.

United States. Roughly a dozen states require everyone’s permission, and the published lists disagree about which. If one person is in such a state, the organiser needs everyone’s agreement. A visible bot does not achieve this.

Voice recordings, and why speaker names are the trigger

Putting a name to a voice is the same act that makes a stored voiceprint valuable. Where a tool keeps a lasting record of how someone sounds, that recording is biometric data under the GDPR, and it needs clear, written permission from each person. Several tools here keep one. Microsoft Teams does it properly, with permission, export, deletion and a one-year expiry; Avoma, Otter, Plaud and Pocket keep one too. Plaud needs singling out, because someone else gets a voice profile built the moment you type their name, nobody asks them, and its API can hand out the underlying voiceprint.

Circleback does the same thing, and documents it in a help article about correcting names. Fix a speaker’s name once and, in its own words, the participant’s voice is saved and recognised automatically in later meetings they are invited to. Nothing in its privacy policy or its data processing agreement mentions voice or biometric data. The person whose voice is stored never appears in the transaction, and that is the arrangement the GDPR requires written permission for.

It leaves a trade-off. The tools that name speakers best carry the most legal exposure. The tools that guess from a calendar carry less, and give you worse information that can still end up as a name on a customer record.

One correction to the picture we painted before. On 1 April 2026 a US appeals court ruled unanimously that a 2024 change to Illinois biometric law applies retroactively, so recording someone repeatedly counts as one offence rather than thousands. Cases brought under that law fell by roughly seventy percent year on year. The risk is real and much smaller than the headline figures suggested.

Where the court cases stand

Otter. Four class actions filed from August 2025 were combined into one case in California. The hearing on Otter’s request to have it thrown out was postponed four times and finally took place on 3 August 2026, with reports of the judge unconvinced that the plaintiffs lacked the right to sue. On 13 August 2026 the court ruled: it dismissed some claims and let the core wiretap, Californian privacy and Illinois biometric claims go ahead. Coverage saying the case was argued in May is wrong; the court record shows that hearing cancelled.

Fireflies. The position is worse than a single dismissal suggested: one case was withdrawn in March 2026, and the litigation then moved rather than ended, with a case in northern Illinois now the lead. Its combined complaint, filed 24 June 2026, claims the company collected and kept voiceprints without the notice, permission and safeguards the law requires. Worth knowing for accuracy: the complaint names a “Speaker Recognition” feature that Fireflies advertises nowhere; what its privacy policy does say is that outside suppliers analyse how participants sound to tell them apart, that this may count as biometric information in some places, and that it is deleted after three years.

Granola. A case was filed in California on 30 July 2026, and we confirmed it in the public court record. It claims breaches of federal and Californian privacy law. It says AI training is switched on by default on the free and business plans, with each person having to turn it off themselves. And it quotes Granola’s own marketing line, that other people in the room will not know it is there, as evidence that staying invisible was a deliberate commercial choice. These are claims, not findings. They matter here because the case is built on the same botless design that makes the product attractive.

Two dates that have already passed

The EU’s amendments to the AI Act were published as Regulation (EU) 2026/1744 on 24 July 2026 and took effect on 27 July. They push the main obligations for higher-risk AI to 2 December 2027, and to 2 August 2028 for a second group. They do not change which systems count as higher risk. Only the deadline moved.

Two obligations are already live.

Transparency obligations took effect on 2 August 2026. For AI systems already on sale before that date, the duty to label AI-generated content applies from 2 December 2026. Whether a particular notetaker falls under them is a question for your lawyer, and one for now rather than later.

Using AI to read employees’ emotions at work has been banned since 2 February 2025, and that has not changed. If a vendor offers you sentiment scoring, engagement measurement or mood analysis of your own staff meetings, raise this before the conversation goes any further.

What to do

1. Write down your recording policy before buying anything. Say when recording is allowed, how you ask permission, and who may read the transcripts.
2. Tell people at the start of every recorded meeting, whether or not there is a bot to see.
3. Read the data processing agreement. Confirm the vendor acts on your instructions rather than its own.
4. Turn off AI training where your plan allows it, and negotiate it into the contract where it does not.
5. Find out what your people already use. Some of them are already running one of these.

Choosing

Do you meet on more than one platform? If everything happens in one place, look at what that platform already gives you. If not, you need something separate.

Do you meet clients often? A visible bot changes a first conversation. Botless avoids that and moves the whole question of permission onto your people. Given where the court cases are heading, write the policy first.

Do the notes need to reach another system? Then what you can export decides it. Fathom, Avoma, Leexi and Google’s transcript feature are the four that give you something usable. Everything else means matching names yourself.

Do you meet in person? Most of these tools fail in a room and give no sign of it. Test that specifically before buying.

Which languages? Otter is out for Dutch. Google’s notes feature is out for Dutch. Notion gives you no speaker names outside English. For everything else, assume nobody has tested it and test it yourself. If the language question and the export question point at different tools, the language question wins.

Recommendation by situation

Most companies fit two rows. Where they disagree and one of them is the language row, the language row wins: a Flemish sales team on several platforms starts with Fireflies, and prices the name-matching by hand.

Evaluation checklist

Before you sign:

• Trial run on the platforms you use
• A real Dutch or French meeting, comparing the raw transcript against the summary, because the summary always looks better
• An in-person test with at least four people around one microphone, checking whether everything is credited to one person
• Export one real meeting and look at the raw data. Does each line carry an ID, or only a name?
• Ask what it matches on when writing to your CRM, and what it does with two companies from the same group on one call
• Price confirmed, including extras, AI credits and minimum numbers
• Leaving tested during the trial: cancel, download everything, confirm the billing stops
• Data processing agreement read by your lawyer
• AI training can be switched off on your plan, and is
• Security certificates requested by number, not accepted as a badge
• Country of storage confirmed for recordings, transcripts, summaries and AI processing
• Voice recordings: who is recorded, who agreed, how long it is kept, how it is deleted
• Recording policy written and approved
• Admin controls tested: single sign-on, removing people who leave, and whether a blocked tool stays out
• Found out what staff already use

Methodology

Most buyer’s guides in this category are assembled from other buyer’s guides. Ours started from the vendor documents, the court records and the users themselves, and the difference decides which claims here you can act on.

Where each kind of claim comes from

What a tool does comes from the vendor’s own documentation, technical references and privacy policies, read directly on 5 August 2026, Circleback’s on 1 September 2026, and re-checked for prices, platform rules and court dates on 6 October 2026. Where a vendor says nothing, we say the vendor says nothing rather than filling the gap. A vendor stating that it does not do something, and a vendor staying quiet, are different facts, and we keep them apart.

What a tool is like to live with comes from users, checked as described below.

The legal position comes from court records and the official European journal, not from news coverage. The most detailed account we found of one vendor’s court case was published by a competitor, and coverage of another has the hearing on a date the court record shows was cancelled a week beforehand.

We do not test tools in a laboratory. The tests in the checklist are the ones only you can run, on your own meetings, in your own languages.

How we checked the user reviews

Vendors in this category work hard to shape what you read, so checking who wrote something is part of the work rather than a footnote. Every source was sorted into three groups before we used anything it said.

Group 1, quotable. A reviewer with a real name or a long-standing account, writing on a site where disagreement is allowed to stand. The claim has to be specific, with a plan, a date, a described fault or a meeting type attached. There must be no sign of payment or any connection to the vendor. And at least one other independent person has to back it up.

Group 2, reportable. A specific claim from a plausible reviewer, with only one source, or on a site where vendors are known to ask for reviews. It appears here as something people report, never as an established fact.

Group 3, discarded. Anything written by someone earning commission, by a competitor, or by a reviewer whose job does not fit the claim. Anything on a site the vendor invited them to, or arriving in a sudden burst of similar reviews. Any praise or criticism with no explanation attached. And anything on a forum that turned out to be fake.

We threw away more than we kept. Six patterns came up often enough to name, because they will shape whatever you read next.

Competitors write most of this category’s reviews. Twenty-four companies in and around this market publish independent-sounding reviews about their rivals. Much of it is accurate, and it is still sales copy, because a rival picks which true facts to repeat.

Some vendors pay for their own reviews without anyone breaking a rule. One company here pays 6 to 20 percent commission per device sold through a link, and both of the most detailed reviews of its product declare that arrangement. Another pays 30 percent per referral. Where a vendor pays commission, treat every review as paid until you have checked.

Fake forums now imitate Reddit. One website, three weeks old and already holding thousands of posts under invented usernames, came up in our searches for three separate tools, and its threads contained the most convincing technical detail we found anywhere.

Search summaries invent facts that look quotable. Three times during this research, a summary produced a specific, dated, believable claim that was nowhere on the page it cited. We opened every page.

Review scores can be manufactured without a single fake review. Sites where vendors may ask customers to review sit consistently half a star above sites where they may not, and one product here shows 782 five-star ratings with none at all at two or three stars. The reviews may all be real; the population was picked by the vendor.

“Reddit says” almost always means nobody looked. At least eight discarded sites summarised Reddit without linking a single discussion, while automated access to Reddit fails. We opened it in a browser instead.

What we could not do

We cannot prove who is behind an anonymous account, so this process catches manufactured agreement rather than individual dishonesty. Two review sites block automated access; we read one by hand, and on the other never managed to check which reviews the vendor had requested, and would rather say that than imply we had.

For one product, almost all the usable evidence came from a forum the vendor runs itself. We used it, and you should know why. A complaint that survives on a company’s own forum is a statement against that company’s interest, the posts carry dates and long-standing accounts, strangers confirm each other, and named staff and both founders reply on the record. We took only critical findings from it and no praise. It is still the vendor’s own venue, and it visibly filters competitors’ names, so weigh it accordingly.

Where nothing survived our checks on a question that matters, we say so in the text. Finding no evidence tells you something about a vendor, and it is not a gap to be filled with whichever source sounded most confident.

Where a claim we had published before did not survive, we withdraw it in the text rather than delete it and say nothing.

Why we wrote this

A notetaker solves recording. The harder question comes afterwards: where six months of transcripts live, who can search them, and what they feed. For most companies the answer today is a supplier’s archive, searchable only through that supplier’s website, charged per person, and stranded when the contract ends.

The research for this guide ran into the same problem it describes. Reading a year of user reviews for every tool in this guide, most of what we found was written by the competitors of the product being reviewed, by people earning commission, or by software generating discussions no human ever had. Working out which reviewers were real took longer than reading them. That is the same problem you have inside your transcripts, at a different scale. There is plenty of raw material, and it is worth nothing until something turns it into a record you can trust.

Machinegeist builds AI-native operating models for mid-market companies in the Benelux. Meeting transcripts are one of the richest sources of context a company owns, which is why we did this research for ourselves first. We sell no tools, resell nothing on this list, and our opinion is the product.

If you want a second opinion on your shortlist, or you want to see what happens when meeting notes feed an operating model instead of an archive, talk to us. Thirty minutes, no charge.

Christophe Rosseel, co-founder, Machinegeist

machinegeist.ai · machinegeist.ai/contact · hello@machinegeist.ai

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Prices, features and security certifications in this market change constantly. Confirm the current details with the vendor, and run your shortlist through the checklist on your own meetings before you sign.

Version 2.1.5, published October 2026. Prepared by Machinegeist, Ghent · machinegeist.ai

Sources

Vendor prices and documentation: Fireflies, Fireflies transcript data, Fireflies AI credits, Fireflies privacy policy, Granola, Granola speaker tags, Granola transcription on mobile, Granola API, Granola people and companies, Granola announcements, Granola downloading notes, Fathom, Fathom transcript reference, Fathom release notes, Superhuman acquires Fathom, Fathom on the Superhuman acquisition, Otter, Otter supported languages, Otter speaker identification, Otter privacy policy, tl;dv free plan limits, tl;dv prices, tl;dv privacy policy, tl;dv’s reply to the August disclosure, Krisp, Krisp data handling, Krisp speaker naming, Read AI, Read AI technical reference, Avoma, Avoma technical reference, Avoma speaker identification, Avoma in-person, Plaud, Plaud speaker labelling, Plaud connector documentation, Pocket, Pocket documentation, Pocket voice profiles, Pocket speaker management, Pocket technical reference, Pocket privacy policy, Pocket terms of sale, Leexi prices, Leexi privacy policy, Leexi technical reference, Meetily, Fyxer notetaker, Fyxer prices, Notion AI Meeting Notes, Notion prices, Circleback prices, Circleback API overview, Circleback meeting reference, Circleback transcript reference, Circleback webhook payload, Circleback meeting languages, Circleback participant names and saved voices, Circleback recording, Circleback security, Circleback trust centre, Circleback privacy policy, Circleback data processing agreement, Circleback affiliate programme.

Platform documentation: Microsoft 365 Copilot prices, Teams voice recognition, Teams meeting-room speakers, Teams transcript reference, Teams transcript access, Teams bot approval, Teams external bots, Google Workspace prices, Take notes for me, Take notes for me languages, Meet transcript reference, Meet notes reference, Meet media reference, Meet media admin controls, Google Meet guest screening, Take notes for me settings, July 2026, Take notes for me settings take effect, September 2026, Teams bot protection announcement, Teams bot identification rollout notice, Teams bot auto-block notice MC1459141, Teams bot auto-block rollout, Zoom RTMS overview, Zoom On Behalf Of token FAQ, Zoom Meeting SDK authorization, Zoom RTMS security explainer.

Speech engines: Granola security page, Avoma subprocessors, tl;dv privacy policy, transcription providers, Otter terms of service, Krisp on-device transcription, Fireflies subprocessors, Fathom subprocessors, Whisper per-language error rates, the Whisper paper, Radford et al. 2022, Whisper large-v3 model card, the FLEURS benchmark, OVH completes the Gladia acquisition.

Legal: Otter.AI privacy case record, Otter.AI ruling on the motion to dismiss, 13 August 2026, Cruz v. Fireflies.AI record, Fricker v. Fireflies.AI record, Chamberlain v. Granola record, Regulation (EU) 2026/1744, biometric privacy claims in Illinois, AI meeting assistants and biometric privacy, GDPR and recorded meetings, Belgian recording law, Netherlands recording law.

Independent users and practitioners: Frederik Vincx, Enter Digital, on Dutch transcription, Tony Redmond on notetakers in Teams, Andrés Gorzelany on blocking Otter in Teams, Spiceworks on AI transcription tools, r/sysadmin on Otter.ai, Microsoft Q&A on Otter in Teams, Microsoft Q&A on meeting-room speaker names, Hacker News on Notion desktop audio detection, The Verge on Granola note links, Meetily bug 356, dropped speech, Meetily bug 581, invented English, Meetily bug 675, short words discarded, Chapman University on Read AI, UW–Madison on Read AI, Morgan State on AI notetaking tools, Galaxus test of Plaud Note Pro, Pocket users on transcripts appearing in Welsh, Pocket staff on speaker separation and the free plan, Pocket user on a three-person meeting, Pocket users on recordings stopping, Pocket free-plan change and reversal, ChannelLife hands-on review of Pocket, Pocket funding, tl;dv data exposure disclosure, shadow AI governance risk, ISACA, AI notetaker adoption statistics.