What is the main difference between AEQI and Patricia?

Patricia is a proactive AI coworker that lives in Slack or Teams: it records and recaps meetings with a decision log, spots tasks across channels before anyone files them, runs scheduled workflows like daily recaps and month-end invoicing, and asks for approval before it publishes or spends. It is one very capable teammate inside a chat workspace you already run. AEQI is the company underneath: agents holding distinct roles with their own permissions, a quest ledger you can open and redirect, memory owned by the Company, a live website, and a server provisioned for you alone.

How does Patricia's pricing compare to AEQI's?

As of August 2026, Patricia charges $45 a month for the entire company with all seats included, metered as 20,000 monthly credits that roll over, and publishes example unit costs — roughly $0.34 for a drafted follow-up email, $0.90 for a LinkedIn post, $7.09 for a full launch campaign. AEQI publishes €50, €200, and €800 a month for 1,250, 5,000, and 20,000 credits across 1, 4, and 16 seats. The credit units are not the same: theirs meters model work inside someone else's chat app, ours meters one cent of execution on infrastructure dedicated to your Company.

Patricia says 'everyone has AI, nobody has the company context.' Isn't that AEQI's argument too?

It is close, and it is a good argument — shared company context beats a room full of individual chat accounts. The difference is where the context lives and what it can do. Patricia's context is what it has read in your channels; it makes one coworker better informed. AEQI's memory is the Company's own: decisions, lessons, files, and operating history that every role draws on, that survives any agent being replaced, and that sits next to the authority to act on it. Context that only informs is a better assistant. Context bound to roles, permissions, and a ledger is an institution.

Who should choose Patricia over AEQI?

A team of up to about fifty people that already has a company and wants the meeting recaps, the follow-ups, the recurring reports, and the invoicing handled inside the chat where they already work — with approval gates before anything is published or spent. Patricia is aimed squarely at that and is priced for it. Choose AEQI when the constraint is not the chat but the company: when the roles are unfilled, the operating history lives in nobody's head, and the work needs somewhere of its own to run.

AEQI vs Patricia

Patricia calls itself “the proactive AI coworker for your whole team”, and the emphasis is on proactive. It lives in Slack and Teams, records and transcribes meetings and writes recaps with a decision log, spots work across channels before anyone files a ticket, runs scheduled workflows — daily recaps, weekly reports, month-end invoicing — and connects to more than a thousand tools. Before it publishes anything or spends anything, it asks. The target is solo founders up to teams of around fifty.

Its differentiating line is a good one: “Everyone has AI. Nobody has the company context.” That is nearly our own argument, made by someone else, shipping. It deserves a straight answer rather than a dismissal.

Whose context, and what it is allowed to do

Patricia’s context is the reading of your channels: what was said, decided, and promised, held so that one coworker is better informed than any individual chat account could be. That is genuinely useful, and it is a real gap in most teams.

AEQI puts the same idea one layer down. Memory belongs to the Company — decisions, lessons, documents, and operating history in one graph that every role can draw on, that outlives any agent being replaced, and that sits beside the authority to act on it. An agent recalls the pricing decision and holds the role that lets it change the pricing page, with the change written into a ledger you can read afterwards. Context that only informs makes a better assistant. Context bound to roles and a record makes an institution.

One coworker or a staffed chart

The structural difference is headcount shape. Patricia is one assistant, however capable, inside a workspace you own. There is no org chart, no delegation from one agent to another, no distinct permissions per role, no infrastructure that belongs to the company it serves. AEQI provisions the company itself: agents in named roles with their own tools and permissions, work decomposed into quests that can be opened and redirected mid-flight, a live website, and a server with your own billing on it.

What the price is a price of

As of August 2026, Patricia is $45 a month for the whole company, every seat included, metered as 20,000 credits with rollover, and it publishes its unit economics openly — about $0.34 a drafted follow-up email, $0.90 a LinkedIn post, $7.09 a full launch campaign. That transparency is admirable and rare.

AEQI publishes €50, €200, and €800 a month for 1,250, 5,000, and 20,000 credits at 1, 4, and 16 seats, plus €20 per extra 1,000. Read the two pages naively and $45 for 20,000 credits beats €50 for 1,250 by a mile. The units are not the same. A Patricia credit buys model work inside a chat app somebody else operates; an AEQI credit is one cent of execution on infrastructure provisioned for your Company and paid for monthly whether it runs or not, carrying your data, your files, your site, and your integrations. Both are honest meters of different objects.

Where Patricia is ahead today

Being in the room. Patricia is addressable in the channel where the work is already being discussed, and it acts without being asked — noticing the commitment nobody filed. Our Slack integration posts outbound today; an AEQI agent cannot yet be spoken to in a channel. Their connector count is also far above ours. On chat-native reach, they are ahead, and we would rather say so than let you find out.

The verdict

Choose Patricia if the company exists and the chat is where it runs: meetings recapped, follow-ups sent, reports and invoices on a schedule, approvals asked for before anything ships. Choose AEQI when what is missing is the company — roles that are actually filled, a ledger instead of a thread, memory that compounds at the institution rather than the individual, and infrastructure that carries your name.

All figures as of August 2026, taken from patricia.app.