I built AI meeting notes that refuses to claim “true E2EE”
Most AI meeting tools either:
1) quietly process your audio in the cloud and call it “secure”, or
2) claim “E2EE” while still running cloud ASR.
That’s impossible. If a model must hear your audio, it isn’t end-to-end encrypted during that moment.
So I built AegisMeetings around a named exception instead of marketing theater:
• Sealed at rest (client-side crypto)
• One Honest Processing Window for ASR + summary
• Wipe after the job — no plaintext retained
I’m a solo founder. Product is live. Revenue is $0 for now (just opening membership).
Looking for blunt feedback from privacy-minded founders / consultants / anyone who can’t put meeting audio into a black-box SaaS:
What’s the one thing that would make you trust (or still reject) a cloud ASR product?
what would make me trust it: the honest window being verifiable rather than just stated. anyone can write "we wipe after the job". what moves a sceptic is something they can check without taking your word for it, like a per-meeting receipt showing when the window opened and closed, a retention number in writing, ideally an attestation from someone who is not you.
your positioning raises your own proof bar, which is the awkward part. you are selling on being more honest than the category, so prose alone starts to read like a politer version of the thing you are calling out.
on your follow-up about people not feeling the pain yet, i think that is the real constraint and it is not fixable by explaining the window better. honesty does not create demand. it wins the comparison once someone is already shopping on privacy. so the wedge is probably people who have already been told no: legal teams that blocked the obvious incumbent, consultants with client confidentiality clauses, anyone in a regulated sector. they arrive pre-motivated, and they are the only ones who will actually read the ASR paragraph.
You're right on both counts.
Honesty doesn't create demand. Zero users (or near-zero) proves that — being open about the processing window doesn't make someone who wasn't shopping for privacy start caring. The people who will actually read the ASR paragraph are the ones already stuck: legal said no to the obvious tool, client confidentiality clauses, regulated work. Everyone else just wants the transcript.
On trust: "we wipe after the job" is cheap. A sceptic should be able to check something. Today I can describe the threat model and the code path; I don't yet ship a per-meeting receipt (window open/close) or a third-party attestation. Calling that out is fair — selling on honesty raises the proof bar, and prose alone isn't enough. Receipts / retention in writing / independent attestation are the next bar if this is going to be more than a nicer slogan.
So: agree on the demand wedge, agree on the proof gap. Not trying to win the argument with better wording.
the receipt that actually moves a sceptic isn't proof you deleted it, it's proof you couldn't have kept it. an object-store lifecycle rule with a hard TTL that your app has no permission to override is a much stronger claim than a per-meeting log, because it moves the guarantee from your conduct to your config, and config is checkable by someone who doesn't trust you.
per-meeting receipts still worth shipping, just append-only and hash-chained. otherwise a backdated deleted_at looks identical to the real thing.
This is a sharp distinction — and the right one for a sceptic.
What actually moves trust isn’t a claim that we deleted something, but a design where we couldn’t have kept it: a hard object-store TTL the app has no permission to override beats a per-meeting log, because the guarantee sits in checkable config, not in our conduct. Agreed that per-meeting receipts are still worth shipping — but append-only and hash-chained; otherwise a backdated
deleted_atis indistinguishable from the real thing.We’ll keep this in how we think about the trust boundary. No timeline from me here, but thank you for putting it this clearly.
The interesting part is that you’re not selling “more AI notes” — you’re selling a different trust model.
The challenge will probably be finding the users who care enough about that distinction to change tools. Privacy can be a strong wedge, but only when the risk feels real to the buyer.
Yeah, that matches what I’m seeing.
Most people still shop for “better notes / faster transcript.” The trust-model distinction only matters after someone has already felt the risk — a leak scare, a compliance review, a client asking where the audio goes, etc. Until then, Honest Processing Window reads like a footnote, not a reason to switch.
So right now it doesn’t feel like the market is rejecting the product so much as… not feeling the pain yet. Few people even get far enough to compare “fake E2EE” vs an explicit plaintext ASR window.
I’m trying to decide whether to:
keep leading with the trust model and hunt for the smaller segment that already cares, or
lead with transcript quality / workflow, and treat honesty as a trust filter for the ones who dig deeper.
The interesting part is not just making meetings private — it’s defining what privacy claims actually mean in an AI workflow.
The trust barrier seems like the main product challenge here. Curious which audience you think has the strongest need for this: consultants, regulated industries, or security-conscious teams?
That’s a useful split.
On paper, regulated industries have the strongest need — because someone else can force the question “where does the audio go?” Consultants feel it too when a client recording leaking would end the relationship.
But for this product right now, I think the best wedge is security-conscious teams (and consultants who behave like them): people who already distrust soft “encrypted / E2EE” claims in AI tools, and will actually read what a plaintext ASR window means.
Regulated buyers may need the honesty most, but they also buy on audits, legal review, and procurement. That’s a slower motion than what we’re set up for today.
So my working order is:
security-conscious teams
consultants handling sensitive client meetings
regulated industries later, once the trust story is boringly documented
I appreciate you walking through your thinking. It was interesting to see how you're sequencing the market rather than treating every potential customer the same.
I'd enjoy comparing a few more thoughts outside the thread. What's the best email to reach you on?
Thanks! You can reach me at service@aegismeetings.com. Looking forward to hearing your thoughts.
Thanks! I’ve just sent it over.
Looking forward to hearing your thoughts whenever you have a chance.
Transparency in AI outputs is huge. I’m building Finsight AI, and SMBs also need to trust that forecasts aren’t ‘black boxes.’ Your honest processing window idea resonates a lot.
That’s exactly the tension I’m seeing too — SMBs care both about where data is processed and whether the logic behind outputs is verifiable. With Finsight AI, they often ask ‘Can I trust this forecast?’ before anything else. Transparency in both processing and reasoning seems key.
Appreciate that! Finance and raw audio definitely share the exact same pain point—nobody wants their crown jewels sitting in a cloud black box.
"Trust, but verify" seems to be the only real path forward for B2B AI now. Checked out Finsight AI—solving that transparency problem for SMB financials is huge.
Quick question on your end: when talking to your users, do they care more about where the data is processed, or how verifiable the output logic is?
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Thanks for your comment! I appreciate it, but it's not relevant to the topic I'm discussing here. Best of luck with your business.