What does Wispr Flow answer before product, sales and leadership adopt it?
A skeptical read of Wispr Flow’s live homepage through a Product Manager, an Account Executive and a Senior Leader / Founder. All three are treated as both buyer and day-to-day user because Flow is self-serve enough to purchase individually while also supporting team rollout. Each underlying decision question is counted once after checking the entire homepage.
Wispr Flow is much stronger than a typical AI-product homepage at demonstrating what the product actually does. It earns high confidence for category clarity, cleanup behavior, cross-app use, personalization, security credentials, objection handling and low-friction trial. It loses weighted confidence on the questions that become more important once the demo looks impressive: whether cleanup ever changes meaning, whether 4× speed survives real review time, how typical the case-study outcomes are, what data is retained, what paid plans cost and how a team rollout behaves after the novelty wears off.
Topic coverage is 35/43 = 81%, but coverage is not treated as the quality score.
Coverage tells you what is mentioned. Confidence tells you whether it is convincing.
Strong answers earn full credit, partial answers earn half credit, and unanswered questions earn none. Purchase blockers count 3×, important questions 2×, and supporting details 1×. That prevents a long FAQ from cancelling out a smaller number of high-stakes adoption risks.
Strong = 1 point. Partial = 0.5. Unanswered = 0, before decision-importance weighting.
Open questions remain in the denominator at their importance weight unless you mark them not relevant.
Purchase blocker ×3. Important ×2. Supporting/detail ×1.
Weighted decision confidence by homepage section.
Section scores use the same weighted model as the whole page. A visually impressive section can still score poorly when it creates high-stakes questions that the rest of the homepage does not resolve.
Don’t type, just speak.
The first screen gets the product across unusually fast. The headline is almost category-level simple, the subheadline explains the transformation, the platform badges remove compatibility anxiety, and the messy-speech-to-polished-writing example gives the visitor a mechanism to believe. The risk created by that same demo is semantic control: if Flow is doing more than transcription, I need to know how it behaves when the cleanup is wrong.
What I understand in 5 seconds
I can talk through an unfinished thought and Flow will turn it into cleaner writing inside the apps where I already work.
I can dictate prospect and internal messages much faster without sending obvious voice-transcript junk.
This is a cross-device productivity layer, not a single-app transcription tool, with a plausible path to team use.
What the hero currently shows
A large “Don’t type, just speak” promise, a one-sentence product definition, free-download CTA, Mac/Windows/iPhone/Android availability and a before/after transformation from rambling speech into polished prose. A company-logo strip adds credibility immediately below.
It demonstrates the product’s central transformation. The visitor does not have to infer what “AI dictation” means because the raw speech and cleaned result make the value visible.
What it proves vs what it only claims
Recommended hero visual
Keep the current before/after concept, but turn it into a 12–15 second muted loop that shows Flow operating in three real contexts: a Slack update, a Linear/Notion product note and a Gmail sales follow-up.
- First frame: cursor active in the real app with a tiny Flow listening state.
- Sequence: messy speech appears briefly, then filler/correction highlights collapse into the final written message.
- Safety moment: a corrected date or number visibly replaces the earlier one, with an undo affordance shown for one second.
- Final frame: three finished outputs with a small “same Flow, every app” label.
- Behavior: autoplay muted, loop continuously, keep the app chrome and cursor position stable so the transformation is easy to follow without reading body copy.
The current hero proves cleanup. This version would also prove cross-app fit and make semantic control visible, closing one of the highest-weight unanswered questions on the page.
Don’t type, just speak.
The hero makes the category instantly legible: Flow is voice-to-text AI that turns speech into polished writing in every app. The messy-speech-to-clean-output example is the strongest evidence on the page because it shows the mechanism rather than asking visitors to imagine it. The main remaining risk is fidelity: the more aggressively Flow cleans up speech, the more a serious user wonders whether it can silently change meaning.
PM · BOTHDo I immediately understand what Flow does and where it works?Strong · ×3⌄
I need to know whether this is a transcription utility, a writing assistant or something narrower before I spend attention on the rest of the page.
The hero calls Flow a voice-to-text AI that turns speech into polished writing in every app, then shows Mac, Windows, iPhone and Android availability.
I understand the category, the core transformation and the breadth of where I can use it without hunting for an explanation.
PM · BOTHWill it turn messy spoken thoughts into polished writing rather than a raw transcript?Strong · ×3⌄
The value disappears if I dictate quickly and then have to spend the same amount of time cleaning filler, repetitions and corrections out of the result.
The hero puts a rambling spoken paragraph beside a much cleaner written version, making the cleanup behavior visible rather than merely claiming it.
I can see the core mechanism that should make Flow materially better than standard transcription.
PM · BOTHCan I trust the cleanup not to change what I actually meant?Partial · ×3⌄
The more Flow rewrites my speech, the more damaging a subtle meaning change becomes in a roadmap decision, deadline, requirement or stakeholder message.
The demo shows sensible cleanup and the later FAQ explains that Flow catches corrections such as changing 5pm to 6pm, but the page does not quantify fidelity or show safeguards for incorrect semantic edits.
I see evidence that Flow understands corrections, but I still cannot judge how safe aggressive cleanup is for important details.
AE · BOTHCan I use it across the places I write all day, not just in one editor?Strong · ×3⌄
A dictation tool that only works in its own composer adds another step to prospecting, follow-up and internal communication instead of removing one.
The page repeatedly says Flow works in every app and later names Gmail, Slack, iMessage, Notion, terminals and AI tools because it types wherever the cursor is.
I can expect Flow to sit inside my existing writing workflow instead of forcing me into a separate workspace.
LEAD · BOTHIs this meaningfully different from the dictation already built into my devices?Strong · ×2⌄
I already have free voice typing. Flow only deserves another tool, habit and subscription if the output is materially better, not just marginally faster.
The page visually shows cleanup and later explains specific differences: corrections, formatting, personal vocabulary and consistent behavior across apps and devices.
I have a concrete reason to evaluate Flow separately from Siri or Google voice typing.
LEAD · BOTHIs there enough evidence that this is more than a clever demo?Partial · ×2⌄
A polished demo can make almost any AI product look reliable. Before I normalize it across a team, I want evidence that real professionals keep using it successfully.
The hero says it is used by professionals at companies such as Microsoft, Amazon, Notion, Klarna and Vercel, and the page later includes named users and case studies. The logo strip itself does not prove company-wide adoption.
There is credible social proof, but I should not read the employer logos as proof that those companies bought or standardized on Flow.
PM · BOTHWill it work across Mac, Windows, iPhone and Android?Strong · ×2⌄
I move between laptop and phone, and team members may be on different operating systems. A single-device product would fragment the workflow.
The hero explicitly lists Mac, Windows, iPhone and Android.
Cross-device availability is clear before I have to investigate setup details.
4x faster than typing
The 45 WPM keyboard versus 220 WPM Flow comparison gives the speed story a memorable number. The weakness is that raw words per minute is not the same as time saved in a real workflow after reviewing output, correcting mistakes and switching between apps. Later case studies help, but they do not fully validate the headline claim for product, sales or leadership work.
LEAD · BOTHIs the 4× faster claim based on a credible comparison, not just an ideal WPM number?Partial · ×3⌄
A 4× claim is strong enough to influence a purchase decision, so I need to know whether it reflects measured end-to-end work or simply compares speaking speed with average typing speed.
The page gives exact WPM figures, which is better than an unsupported adjective, but it does not show the test method, sample, task type or correction time behind those numbers.
I understand how Wispr gets to the headline ratio, but not whether that ratio survives a realistic workflow.
PM · BOTHDoes speaking faster still save time after reviewing and correcting the output?Partial · ×3⌄
I care about finished work, not raw text generation. If I have to carefully reread every sentence for AI edits, the claimed time saving could disappear.
Later proof shows dramatic message-output and productivity gains for selected customers, but the homepage never measures correction/review overhead directly.
There is evidence that some users save substantial time, but the page does not close the loop between dictation speed and net time saved.
AE · BOTHWill this actually increase my sales throughput, not just my raw word count?Partial · ×3⌄
Writing speed only matters if I can follow up faster, update notes more easily or spend more time with customers. More words per minute is not automatically more revenue.
The Clay case later cites 20% more customer calls per day and estimated annual savings, which points toward operational value, but the homepage does not explain the sales workflow or causal link.
I can see a plausible sales-productivity outcome, but not yet what part of my daily process Flow changes enough to produce it.
PM · BOTHDoes the speed advantage hold for longer, structured work like specs and strategy docs?Partial · ×2⌄
A lot of my highest-value writing is structured and requires precise thinking. A short message demo may not predict whether voice is faster for a PRD, decision memo or strategy doc.
The page shows long blocks of generated text and claims Flow works anywhere, but the dominant examples remain messaging-oriented rather than structured product work.
I believe Flow can generate longer text, but I do not have strong workflow proof for the documents that matter most to me.
Speak at the speed you think, in every app, on every device.
This section demonstrates three important behaviors: Flow tolerates false starts and corrections, cleans up the result as you speak, and works wherever the cursor is. That makes the workflow easy to picture. The unanswered risk is control: there is no equally clear explanation of how a user constrains rewriting or recovers when cleanup changes a date, qualifier or other important detail.
PM · BOTHCan I ramble, self-correct and pause naturally without manually cleaning everything up?Strong · ×3⌄
If I have to plan sentences before speaking, I lose the biggest advantage of voice: getting an unfinished thought out quickly while I am thinking.
The demo intentionally includes filler, false starts, repeated words and a corrected deadline, then shows a concise final message.
The page convincingly demonstrates that imperfect speech is an intended input, not an edge case.
AE · BOTHWill it automatically format messages so I can send them without another editing pass?Strong · ×2⌄
Sales messages need readable paragraphs, punctuation and a tone that does not feel like a voice transcript. Extra cleanup at scale would kill adoption.
The section explicitly shows filler removal, corrections, repetition cleanup, punctuation and message formatting.
I can reasonably expect the first draft to look written rather than transcribed.
PM · BOTHDoes it work anywhere I can type without plugins or integrations?Strong · ×3⌄
I do not want to maintain separate integrations for Slack, docs, product tools, browsers and AI assistants just to use dictation consistently.
The page says Flow works anywhere you can type with no plugins and syncs across Mac, Windows, iPhone and Android.
The adoption surface is broad and does not depend on a long integration checklist.
AE · BOTHIs it responsive enough to feel faster than typing in real use?Partial · ×2⌄
Latency matters because dictation feels broken when text arrives slowly or cleanup lags behind the conversation in my head.
The rendered workflow is designed to feel immediate and the page uses speed language throughout, but it does not state real transcription or cleanup latency.
The experience appears responsive, but I do not have a concrete expectation for delay under normal conditions.
PM · BOTHCan I control how aggressively Flow rewrites what I say?Unanswered · ×3⌄
Sometimes I want polished prose; other times I need exact wording, quoted language or a requirement preserved verbatim. One global cleanup behavior creates risk.
Show the control surface for rewrite intensity or modes, with examples of verbatim, light cleanup and polished output, plus whether settings can differ by app.
PM · BOTHWhat happens when Flow changes an important detail incorrectly?Unanswered · ×3⌄
A wrong date, number, name or qualifier can create real downstream work. I need recovery to be faster than manually retyping the sentence.
Show how users spot, undo or correct a bad transformation, whether the original transcript is recoverable, and how that correction affects future behavior.
Built around how you work, not how we think you should.
Flow goes beyond generic transcription by showing app-specific tone, personal vocabulary, snippets and 100+ languages. This is particularly useful for sales names and product jargon. Team-level standardization is less clear: the page visually hints at sharing vocabulary, but it does not establish how much can be centrally managed across a company.
AE · BOTHWill it reliably get customer names, companies, acronyms and jargon right?Strong · ×3⌄
Names and account-specific terms are exactly where generic dictation creates embarrassing errors. If I am constantly fixing them, I will stop using the tool.
The page says Flow learns unique words and names automatically, lets me add them manually, and visibly shows a vocabulary workflow.
The product directly addresses one of the biggest day-to-day failure modes for sales dictation.
PM · BOTHCan it adapt my tone depending on whether I am in Slack, email or a document?Strong · ×2⌄
My writing style changes by context. If every dictated message sounds equally formal or generic, I still have to rewrite it.
The page says Flow adapts to how I write in different apps and lets me set different styles for messages, work chats, emails and more.
Context-specific tone is a real product capability, not just a generic personalization claim.
PM · BOTHCan I save repetitive text and expand it by voice?Strong · ×1⌄
Small repeated pieces such as links, meeting instructions or bios create unnecessary typing even when the surrounding message is dictated.
The homepage explains snippets and says spoken shortcuts can expand reusable text instantly.
There is a concrete productivity feature for repeated content beyond pure transcription.
LEAD · BOTHCan a team share or centrally manage important vocabulary and conventions?Partial · ×2⌄
Shared product names, customer terms and company language are more valuable if everyone benefits. If every employee has to train their own dictionary, rollout becomes fragmented.
The visual includes a “Share with team” action, suggesting team vocabulary exists, but the homepage does not explain governance, permissions or central administration.
Team standardization appears possible, but I cannot yet judge how manageable it is at scale.
PM · BOTHCan I switch between languages without breaking the workflow?Partial · ×2⌄
International teams and customers make language switching common. Auto-detect is only useful if it does not create a new source of correction work.
The page supports 100+ languages and says it can auto-detect, but the FAQ recommends selecting one specific language for best accuracy and switching manually when needed.
Multilingual support is broad, but seamless automatic switching is less reliable than the top-line copy initially suggests.
Your voice stays yours.
This is a strong trust section: the homepage clearly says data is never sold, lets users choose whether data helps improve Flow, and displays SOC 2 Type II, HIPAA and ISO 27001 credentials. For leadership, the unresolved questions shift from certification to data lifecycle: what is retained, whether audio persists, where processing occurs and what the default training posture is.
LEAD · BOTHIs our voice and text data sold?Strong · ×3⌄
Voice captures sensitive internal thinking and customer information. Selling or monetizing that data would be an immediate adoption blocker.
The page explicitly states that user data is never sold.
The commercial data-use boundary is clear enough for an initial evaluation.
LEAD · BOTHCan we choose whether our data is used to improve Flow?Strong · ×3⌄
I need control over whether company content becomes training or product-improvement material, especially when employees dictate confidential work.
The homepage says users choose whether data is used to help improve Flow, and the FAQ points to data-sharing preferences in settings.
There is an explicit opt-control rather than a vague assurance that the company 'cares about privacy.'
LEAD · BOTHDoes Flow meet serious security and compliance standards?Strong · ×3⌄
A tool that hears what employees say needs a security posture that can survive procurement and IT review.
The homepage names SOC 2 Type II, HIPAA and ISO 27001 certifications/compliance and links to deeper privacy material.
The page clears the first enterprise-trust hurdle unusually well for a self-serve productivity product.
LEAD · BOTHWhat exactly is retained, for how long, and where is it processed?Partial · ×3⌄
Certification does not tell me the data lifecycle. Retention and processing location can determine whether legal or IT approves rollout.
The FAQ later says Enterprise can use admin-controlled data retention and privacy policies, but the homepage does not state defaults, processing geography or standard retention windows.
I know retention can be controlled on Enterprise, but I still cannot map the normal data lifecycle from the homepage.
LEAD · BOTHIs company content used for model training by default?Partial · ×3⌄
The existence of a choice is good, but procurement still needs to know the default and whether an admin can enforce it across every seat.
The page establishes user choice and later mentions admin-controlled privacy policies, but it does not make the default training state explicit on the homepage.
I see a control path, but not the default exposure before an admin changes anything.
LEAD · BOTHAre audio recordings or transcripts kept after processing?Unanswered · ×3⌄
Employees may dictate customer information, internal plans or confidential conversations. Persistent audio creates a different risk profile from transient processing.
State whether raw audio is stored, for how long, whether transcripts persist, and how deletion works for individual and enterprise accounts.
From the first people to use it.
The homepage has unusually concrete outcome proof for a consumer-style productivity tool: Steven Bartlett is paired with 90% faster message output and two extra productive hours per day, while Clay is paired with 20% more customer calls and an estimated $3.08m in annual savings. The issue is representativeness. These are striking examples, but the page does not show how typical they are, how the calculations were made, or whether team adoption stays high over time.
LEAD · BOTHDo customers show measurable productivity or economic outcomes?Strong · ×3⌄
If I am considering team rollout, I need proof that faster dictation changes output or capacity, not just that people enjoy the product.
The page pairs named cases with quantified outcomes: 90% faster message output, two extra productive hours per day, 20% more customer calls and estimated annual cost savings.
There is genuine outcome-oriented proof to build a business case from, rather than only testimonials about liking the product.
AE · BOTHIs there proof that Flow improves customer-facing sales work?Partial · ×3⌄
My best reason to adopt is more high-quality customer activity, not writing novelty. I want evidence tied to sales work specifically.
The Clay card cites 20% more customer calls per day and large estimated savings, but the homepage does not explain which users, tasks or Flow behaviors produced that result.
There is relevant sales-adjacent proof, but the causal workflow is still too compressed to benchmark against my team.
PM · BOTHIs there proof from people doing product work like mine?Unanswered · ×2⌄
Product work has different writing patterns from creator messaging or sales activity. I want evidence around specs, product decisions, stakeholder updates and product-tool workflows.
Add a product-manager case or mini proof block showing weekly use across Linear/Jira, Slack, Notion/Docs and AI tools, with time saved and examples of output quality.
LEAD · BOTHAre the headline results typical enough to use in a business case?Unanswered · ×3⌄
Exceptional case studies are useful for possibility, but budget decisions need an expected range. If these are outliers, I can overestimate payback dramatically.
Show median or typical active-user outcomes, sample sizes, measurement windows and a short methodology note beside the headline cases.
LEAD · BOTHDoes adoption stick across a team after the novelty wears off?Unanswered · ×2⌄
Voice products can demo brilliantly and still be abandoned because speaking at a computer feels socially awkward or only fits certain tasks.
Show 30/60/90-day active-seat rates, words dictated per active seat, or a customer rollout story that makes sustained usage visible rather than only initial excitement.
Good questions.
The FAQ does real selling work. It answers cross-app compatibility, built-in dictation differences, languages, accents, noisy environments, hardware, privacy, free usage and team plans. It also surfaces enterprise controls that would otherwise be missing from the homepage. The biggest omissions are the actual paid price and what team rollout requires in practice.
PM · BOTHWill it work in the specific apps I already use?Strong · ×2⌄
'Every app' is persuasive, but named examples make the claim easier to trust and help me map it to my actual day.
The FAQ explains the cursor-based mechanism and names Gmail, Slack, iMessage, Notion, terminals, code editors, ChatGPT, Claude and Cursor.
I can map Flow to a realistic multi-app workflow rather than taking 'everywhere' on faith.
AE · BOTHCan it handle accents, fast speech, quiet speech and normal workplace noise?Strong · ×2⌄
If accuracy collapses outside a silent office and perfect speaking voice, the product will not survive actual workdays, travel or shared spaces.
The FAQ addresses accents, fast talkers, whispers, noisy environments and microphone placement, including a candid warning about Bluetooth earbuds.
The page sets realistic operating expectations instead of pretending the microphone never matters.
PM · BOTHDo I need special hardware?Strong · ×1⌄
Extra hardware would add cost and make spontaneous adoption much harder.
The page says the built-in microphone is enough and only suggests closer microphones for very quiet or loud environments.
I can test Flow with equipment I already own.
PM · BOTHCan I start for free without a credit card?Strong · ×2⌄
A frictionless trial matters because voice workflow fit is personal; I need to experience it before deciding whether it belongs in my routine.
The page says Flow is free for 2,000 words per week with no trial countdown or credit card required.
I can run a meaningful personal test without making a purchase decision first.
LEAD · BOTHCan I put the whole team on it with centralized billing and seat management?Strong · ×3⌄
A team rollout needs centralized ownership. Individual subscriptions create expense, offboarding and admin problems.
The FAQ says Pro for Teams includes unlimited dictation, centralized billing and an admin portal for seat management.
The homepage clearly establishes a manageable team-buying path.
LEAD · BOTHCan IT enforce SSO, retention/privacy policies and audit logs?Strong · ×3⌄
Security certifications are not enough if IT cannot enforce company policy across accounts and investigate usage later.
The Enterprise description explicitly lists enforced SSO, admin-controlled data retention and privacy policies, and audit logs.
The page answers several enterprise procurement blockers without forcing me into a sales call first.
LEAD · BOTHWhat does the paid product cost once free usage is not enough?Unanswered · ×2⌄
The homepage tells me the free limit and that Pro unlocks unlimited dictation, but I still cannot estimate personal or team cost without leaving the page.
Put the Pro and team starting prices directly into the FAQ answer, then link to the full comparison for details.
LEAD · BOTHHow much rollout, training and admin work will team adoption require?Unanswered · ×2⌄
Voice is a behavior change. Even if provisioning is easy, I need to know how teams learn when to use it, how quickly users become productive and what admins need to manage.
Add a concise rollout strip: invite seats, set privacy policy, optional shared vocabulary, 10-minute onboarding, then show a typical first-week adoption metric from an existing team.
Start flowing
The final CTA is low friction: users can download Flow for free or try the web demo, with support across all four major device platforms. That is enough to make personal evaluation easy. It does not add much information about setup, but the product’s self-serve motion makes this a smaller risk than the open enterprise-rollout questions elsewhere.
PM · BOTHCan I try the core experience before committing to a paid plan?Strong · ×2⌄
Voice fit is experiential. I need to hear myself use it and see the output before any copy can fully convince me.
The final CTA offers both a free download and a web demo, while the FAQ confirms the free plan has no credit card requirement.
The page gives me two low-risk ways to validate the product immediately.
PM · BOTHIs getting started low-friction enough to test immediately?Partial · ×1⌄
A trial loses value if setup turns into permissions, integrations or a configuration project before I can dictate my first sentence.
The CTA and cursor-based product model imply a simple self-serve install, but the homepage does not actually show the first-run setup or time-to-first-dictation.
I expect setup to be easy, but that expectation is inferred rather than demonstrated.
The five changes most likely to increase decision confidence.
These are ranked from the highest-weight open or partial questions in the report, not from generic landing-page best practices.
1Prove that cleanup preserves meaning, not just grammar.
The product’s strongest capability also creates its biggest product-persona risk: Flow changes what users say into what they meant to write.
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Prove that cleanup preserves meaning, not just grammar.
The product’s strongest capability also creates its biggest product-persona risk: Flow changes what users say into what they meant to write.
There is no clear control story for incorrect dates, numbers, qualifiers or over-aggressive rewriting.
Add one “safe cleanup” demo showing a correction, an undo/revert path and the original speech available for comparison.
“Friday… actually Monday” → final output shows Monday, with a tiny “View original / Undo” control. Follow it with: “Flow cleans up how you say it without losing what you meant.”
2Turn “4× faster” into a defensible productivity claim.
220 WPM versus 45 WPM is memorable, but leaders buy finished-work efficiency, not raw speech speed.
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Turn “4× faster” into a defensible productivity claim.
220 WPM versus 45 WPM is memorable, but leaders buy finished-work efficiency, not raw speech speed.
The page never shows methodology or how much review/correction time is included.
Pair the WPM comparison with an end-to-end benchmark and a short methodology note.
“In a 200-word message task, Flow users finished 3.1× faster including review and corrections. n=___, median of ___ tasks.” Then link to the full test.
3Show typical team outcomes, not only exceptional case studies.
The Steven Bartlett and Clay numbers prove possibility. They do not tell leadership what to budget for.
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Show typical team outcomes, not only exceptional case studies.
The Steven Bartlett and Clay numbers prove possibility. They do not tell leadership what to budget for.
90% faster output and $3.08m savings can read as outliers without a baseline.
Add a compact “What teams typically see” strip with median weekly words, active-seat rate and time saved after 30 days.
“Median active team user dictates ___ words/week and saves ___ hours/week after 30 days. ___% of provisioned seats are still active at day 60.”
4Make the homepage feel built for Product and Sales workflows.
The page says “every app,” but most of the visible examples still feel like generic messaging.
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Make the homepage feel built for Product and Sales workflows.
The page says “every app,” but most of the visible examples still feel like generic messaging.
Product managers do not see PRDs, Linear/Jira updates or decision docs; sales reps do not see CRM notes or prospect follow-up sequences.
Add three persona workflow cards with actual app states and one quantified outcome each.
Product: talk through a rough feature decision → polished Linear update. Sales: dictate post-call notes → clean CRM summary + follow-up. Leadership: dictate on phone → polished email on desktop.
5Close the team-buying loop on price, rollout and data lifecycle.
The FAQ proves that team and enterprise products exist, but a leader still has to leave the page to understand cost and implementation risk.
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Close the team-buying loop on price, rollout and data lifecycle.
The FAQ proves that team and enterprise products exist, but a leader still has to leave the page to understand cost and implementation risk.
Paid pricing is hidden behind the Pricing page, rollout effort is unexplained, and audio/transcript retention defaults are not visible.
Add a concise team-evaluation block after the security section: starting price, setup steps, retention defaults and the controls Enterprise adds.
“Start at $___/user/month. Invite seats, enforce privacy settings, optionally share vocabulary, and users can dictate immediately. Raw audio: ___. Transcript retention: ___. Enterprise adds SSO, audit logs and admin retention controls.”