Why Fidaro
Mainstream AI chatbots are convenient – but they can create real privacy and confidentiality risk
Your prompts can contain sensitive data (even when you don’t mean to)
Problem
Normal questions often include details that identify you, your work, or other people.
Example
“Rewrite this email to Sarah at Acme about the renewal discount and include the updated pricing we agreed on.”
- You accidentally share personal identifiers (names, emails, addresses, credentials)
- You expose confidential business context (customers, pricing, roadmaps, contracts)
- You paste sensitive info you didn’t realize was sensitive until later
- End-to-end encrypted chats — we can’t read your conversations, even if we wanted to.
- Your chat history stays yours — it syncs between your devices fully encrypted, unreadable to our servers.
- Your data, your control — not used for training, ever.
Your data can be used for training (depending on the provider/settings)
Problem
Mainstream AI policies vary: some data can be used for training or evaluation unless you’re on specific plans or settings.
Example
A teammate uses a personal account to paste internal content – your org’s intended settings don’t apply.
- Your data may be used in ways you didn’t intend (policy and product changes over time)
- “Opt out” can be confusing, incomplete, or inconsistent across features
- Teams may assume they’re protected when they’re not (especially across accounts)
- A simpler promise: your chats are not used to train shared models (if that’s true for you).
- Clear, default protections that don’t rely on every user flipping the right switch.
“30 days” isn’t the whole story (retention can change when law and lawsuits show up)
Problem
Most mainstream AI chatbots say they retain chats for a limited window (often around 30 days) — but the reality is that retention can be policy-dependent, product-tier-dependent, and sometimes overridden by legal requirements.
Example
“I deleted that chat.” A retention policy might mean “removed from your view,” not “immediately gone everywhere,” and court orders can change the rules midstream.
- What you think is “temporary” can still be stored in backend logs during the retention window
- Legal preservation orders can force providers to retain logs beyond normal deletion timelines (even for deleted/temporary chats)
- Your privacy ends up depending on court filings, not just product settings
- No readable archive to retain: your history exists on our servers only as encrypted data we cannot decrypt.
- End-to-end encrypted: we can’t read your chats, even if compelled.
- No training: your prompts don’t become part of anyone’s dataset.
Why settle for one model? Different models are better at different things
Problem
Most mainstream chatbots lock you into one provider’s model and one provider’s policies. But the reality is: some of the most impressive new models aren’t all offered by the same companies — and when you look outside the usual options, a common hesitation is simple: I don’t want my private prompts accessible to foreign governments.
Example
“I want to test that model everyone’s talking about – but I don’t have a subscription and I don’t want to send personal/work prompts into a system I don’t trust.”
- You’re limited to a single model even when another is better for your task (coding, writing, reasoning, research).
- Comparing models usually means copying the same sensitive prompt into multiple services (more exposure points).
- You avoid trying certain high-performing models because you don’t want your data stored or reachable under foreign government authority.
- The best of multiple leading open models, picked for your task — all running on Fidaro’s own hardware, so benefiting from a new model never creates a new privacy risk.
- End-to-end encryption: we can’t read your chats, even if we wanted to.
- Encrypted chat history: there’s nothing readable sitting on a server to hand over later.
Mainstream chatbots can shape the conversation (bias, refusals, and “politically correct” omissions)
Problem
Many mainstream chatbots apply broad, one-size-fits-all moderation and ranking. That can lead to answers that feel inconsistent, overly cautious, or subtly shaped.
Example
“Give me arguments for and against X” → you get one side strongly framed, or key points missing.
- The model refuses legitimate requests (“can’t help with that”) in unclear ways.
- You get safe, generic answers that omit important nuance.
- The assistant’s “style” can push a viewpoint, even when you asked for neutral information.
- More control over your assistant experience (tone, strictness, neutrality).
- Less “mystery behavior” — fewer unexplained detours, more direct responses.
- Personalization you control — set the tone and strictness you want, instead of one-size-fits-all moderation.
Every claim above is verifiable —read how end-to-end encryption, confidential computing, and attestation work.