Comparing Multiply with something else
Most agencies do not arrive here from nothing — they arrive from a working setup that someone already champions and already pays for. These pages are for that comparison: what each option is genuinely built for, and where the difference shows up in agency work.
Four questions that separate these tools
Feature grids tend to agree with each other. These are the questions where the categories actually diverge.
Does it start from what the project already knows?
A general assistant starts from an empty box every session, so the person supplies the context by hand each time. The question is not how good the model is — it is who is doing the retrieval, and whether last month's work is still reachable.
Can it run without being asked?
Most tools wait for a prompt. Recurring account work — monitoring, reporting, the weekly scan nobody has time for — only gets done if something runs on a schedule and reports back when there is something worth reading.
Does it adapt to your process, or replace it?
Agencies have their own frameworks, and the ones worth keeping are the reason clients hire them. A platform that standardises those away wins on tidiness and loses the thing being sold.
Whose data is it, once it is in there?
Client material is often embargoed, and sometimes contractually cannot leave a jurisdiction. No training on your data, GDPR compliance and EU data residency are the floor, not a differentiator.
What agencies weigh Multiply against and when to stay put
Six honest comparisons. Each says what the other tool is genuinely built for, where the difference shows up in agency work, and when switching is not worth it.
A ChatGPT alternative
The real incumbent — usually already paid for, often set up by a consultant, with an internal champion attached. The question is not model quality; it is what happens to the output afterwards.
A Claude alternative
Strong reasoning and long-context work make Claude a common choice for strategy and long-form. The gap is everything around the conversation: client separation, shared context, and work that runs on a schedule.
A Springboards alternative
The closest positional competitor, and a credible one — built by ex-agency strategists, weighted towards ideation. The difference is how much of the work either tool covers.
A Waldo alternative
Direct overlap, agency-focused, and strongest on intelligence gathering. The question is whether you need the research or the operation that runs on top of it.
A WPP Open alternative
A holding-company operating system an independent cannot buy and would not want to run its client work on. The alternative is a platform the agency owns.
A Jasper alternative
Built for marketing copy at volume, and good at it. If the job is the process that leads to the copy — research, briefing, delivery — that sits outside a content tool.
One system, not another tool.
AI assistants answer one question at a time. Project tools keep track of the work. Multiply is where the work happens, with your clients, your calls and your process already inside.
| AI assistantsChatGPT, Claude | Project toolsAsana, Monday | Multiply | |
|---|---|---|---|
| A living workspace for every client | No | Partly | Yes |
| Remembers every call, file and brief | Partly | No | Yes |
| Records client meetings and files them by client | No | No | Yes |
| Search 250,000 award-tagged campaigns | No | No | Yes |
| Agents that work on their own, on a schedule | Partly | Partly | Yes |
| Built for agency work: briefs, pitches, clients | No | No | Yes |
| Know-how your agency owns, and that compounds | No | No | Yes |
| Every client’s data kept apart | No | Partly | Yes |
Already use ChatGPT or Claude? Keep them. Multiply plugs in through MCP.
Where to start
The fullest comparison we publish is the buyer's guide: Best AI tools for advertising agencies in 2026, which covers general assistants, agency-native platforms and holding-company systems side by side, and says plainly where Multiply fits among them.
For the platform on its own terms, start with the product. If you are still mapping the category, the glossary defines the terms these comparisons keep using, and the free calculators cover the metrics behind them.
The honest way to compare is on your own work
Bring a live project to a walkthrough and run it against whatever you are using today.