Use cases
AI Assistant for Agencies
An AI assistant for agencies runs multi-client operations at team scale — keeping each client's comms, context, and data separate while a 5–20 person team shares governed access. Arlo remembers each account, drafts client updates for your approval, logs work across client tools, and keeps a per-client audit trail.
Agencies don't run on one client's problems. They run on twelve, all at once, held apart by whoever's paying attention that day. The status update to the retail account, the dev handoff for the fintech build, the PR client who wants a recap by Friday — the work is fine. The overhead of keeping it all straight, across a team, without the wires crossing, is what wears you down. An AI assistant for agencies takes that layer off the team. Arlo runs multi-client operations across iMessage and SMS, Slack, Microsoft Teams, and live phone calls, keeps each account separate, and routes anything client-facing through your approval first.
This is not the solo strategy setup — that's our AI assistant for consultants. An agency is multi-client and multi-person: five to twenty people sharing one operation, where one client's data must never surface in another's thread, and every account needs its own accountable record.
Client isolation by default
The fastest way to lose a client is to leak the wrong context into their conversation. Arlo holds per-client memory that stays walled off: the retail account's roadmap never bleeds into the fintech thread, and a draft for one client is built only from that client's context. Each account has its own scope, its own connected tools, and its own history, so the assistant reasons inside one client's world at a time.
That isolation is what makes shared access safe. A 5–20 person team can all delegate to the same assistant without anyone worrying that an account manager's request pulls up another team's client data.
Per-client context and memory
Running fifteen engagements means fifteen mental models nobody can hold at once. Arlo keeps persistent memory per account: the scope, the stakeholders, the decisions made, the deadlines, and where each thread actually stands. Every morning it posts one briefing of what moved overnight across every client — so the team walks in already knowing which accounts need attention.
When someone says "the campaign brief we sent them," Arlo already knows the client, the document, and what was last agreed. Nobody re-briefs it or scrolls back through three weeks of a Slack channel before replying.
Client updates drafted for approval
Client-facing comms are where trust lives, and one wrong send to the wrong account costs more than it saves. So nothing reaches a client unreviewed. Arlo drafts the update, the recap, the status note — and holds it for a named reviewer before it goes out.
- A client asks "where are we on the launch?" and Arlo assembles the status from Linear, Notion, and GitHub, then drafts the reply for an account lead to approve.
- A weekly client roll-up gets drafted per account, ready for the owner to sign off and send.
- A rescheduling request comes in over text; Arlo proposes times against the team calendar and confirms once approved.
You're still the name on every message. Arlo does the work; your team signs off.
Logging work across every client tool
The follow-through is the part that slips. Arlo connects to Gmail, Notion, Linear, GitHub, and 3,000+ other tools, so work lands in the systems each client runs on. It opens the ticket, updates the record, logs the hours, files the recap — scoped to the right account every time.
Some client tools have no API: the legacy portal, the homegrown dashboard. Arlo can log into those once in a secure browser session and read or update them the way a person would, under the same approvals and audit trail as everything else.
Governed access across the team
When five to twenty people share an assistant, governance can't be optional. Every connection resolves through policy before a run starts, every send or write waits for the reviewer you choose, and every account keeps its own audit trail — every tool call, source, and approval on the record, per client.
| Action | What happens |
|---|---|
| Draft a client update | Held for a named reviewer before sending |
| Update a client's tool | Routed through policy, scoped to that account |
| Onboard a new client's toolset | Connected once, scoped, then available to the team |
| Every tool call and source | Logged in that client's audit trail |
That per-client record is what makes agency work accountable: if a client asks what happened on their account, there's a full trace to show them. On the Team plan, pricing is usage-based; the Business plan adds admin policy, roles, SSO, and audit export for firms that need to govern the whole team.
Onboarding a new client
Winning an account shouldn't mean a week of setup. Point Arlo at the new client's tools — their Slack, their project tracker, their inbox — each connection scoped by policy up front. From there the team delegates to it the same way they'd loop in a coworker, and the new account gets its own isolated memory and audit trail from day one.
It's the same one-colleague model behind our AI assistant in Slack: one AI colleague, every surface, governed by default. Explore other setups in use cases.
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Last updated July 13, 2026