Your team has a way of working. Every other tool makes you leave it at the door.
Every team develops certain ways of operating that aren't necessarily written down anywhere. The framing your strategist uses to position findings. The audience language that took two years of research to get right. The competitive context that everyone on the team just carries. The way your researchers like to structure a screener, or the stakeholders who always need to be named in a deck.
That knowledge exists. It's real and it's valuable. But most tools (AI or not) have no idea it exists, so every time your team gets results back or begins putting together a strategy deck, they're rebuilding from zero. Copying in context. Re-explaining the brief. Prompting AI to find their way back to outputs that actually sound like they came from your team.
Suzy is built differently. A brand-wide knowledge base lets you program your team's way of working directly into the platform — so it's incorporated into every message, draft, and deliverable, for every person, every time.
The cost of starting from scratch
Most teams have done the work to build institutional knowledge. There's a positioning doc somewhere. An audience segmentation framework. A competitive landscape overview. Research archives that capture years of what your customers actually think and how your team interprets it.
The problem isn't that the knowledge doesn't exist. It's that it lives in documents, in inboxes, in the heads of people who've been around long enough to absorb it, and none of that travels automatically into the tools your team uses every day.
So when someone turns to an AI tool to move faster, they're starting from zero unless they've manually loaded in everything the platform needs to know. That manual loading is the bottleneck. And it's the thing that breaks down under time pressure, which is exactly when people are most likely to reach for AI help in the first place.
Giving Suzy the same materials you'd give a new hire
Your knowledge base is where you fix this. Upload the documents your team relies on — your positioning deck, your competitive brief, your audience framework, your research archive, and your onboarding materials — and Suzy draws on them from that point forward. Not in a way where the platform can simply recite them back to you, but in a way where the context inside those documents shapes how Suzy responds, what Suzy surfaces, and what Suzy generates.
Think about it like onboarding. When someone new joins your team, you share those materials so they can operate with full context from the start. The knowledge base does the same thing for Suzy, so when you turn to the platform, you’re turning to an extension of your team that can look at your data through the same lens you do.
The part that makes it personal
Team context is shared across everyone. But how you personally work is yours.
Each member of your team can configure their own preferences: their role, their objectives, the topics they care most about, how they like outputs structured. Suzy remembers those preferences and adapts continuously, so the platform acts differently for your brand manager than it does for your insights manager, even when both are working from the same shared foundation. Think: “Don’t use word clouds in my deliverables.” and “Make sure every insight is framed with our personas in mind.”
This is what makes the combination more than the sum of its parts. Shared context ensures that every output, for every person, is grounded in the same team knowledge. User preferences ensure that foundation gets filtered through how each individual actually works. Consistent and personal at the same time, which is something no general-purpose AI tool is designed to do.
What changes across the platform
Load in the right context, set your preferences, and here's what shifts: chat responses are grounded in your knowledge rather than generic AI defaults. Positioning questions reflect your actual competitive framing. Usage questions reference the competitors you've identified. Screeners draw on the audiences you’ve named. The output stops sounding like anyone could have made it, and starts sounding like your team did.
Your Signals feed surfaces what actually matters to your business. Category signals track the competitors you've named. Industry trends align to the topics you monitor. The difference between Signals with your team's context loaded in and without it is the difference between a firehose and a birdseye view of your category that someone on your team actually curated.
In Stories Studio, shared context and user preferences shape a variety of deliverables in ways that compound. Your tone comes through. Your stakeholders are factored in. A team member who just joined can generate a presentation that reflects years of institutional thinking, because that thinking is now in the platform, not locked in someone else's head.
The thing that compounds over time
Better individual outputs are the immediate payoff, but the deeper value is what happens when your whole team is working from the same foundation consistently.
Outputs hang together in a way they don't when each person is manually rebuilding context from scratch. The study a new analyst runs reflects the same audience framework as the research your most senior strategist built last year. The story generated from new data uses the competitive framing your team has established. Nothing gets lost in translation because the translation is happening at the platform level, not the individual level.
Most tools treat every session as a blank slate. Every prompt is the first prompt. Every output needs to be re-briefed, re-contextualized, re-shaped into something that actually sounds like it came from your team. That overhead is so normalized that most people don't even notice they're doing it.
Suzy is designed to eliminate it. Load in how your team works, and the platform carries it forward. Across every person, every study, every output. The context your team has spent years building doesn't have to stay locked in people's heads. It can live in the platform, available to everyone, every time they need it.
Quick answers: Suzy knowledge base and brand settings
What goes into a Suzy knowledge base? Suzy’s knowledge base is where teams define best practices, key competitors, product lines, brand guidelines, and other preferences. It’s also where teams can upload strategy documents and data from other tools and agencies. Suzy draws on every piece of information in a brand’s knowledge base to inform outputs across chat, Signals, and Stories Studio. It's the primary way to load your team's institutional knowledge directly into the platform.
How is the knowledge base different from user preferences? The knowledge base and brand settings hold shared, dashboard-wide context that shapes outputs for every user. User preferences are individual, where each person configures their own role, objectives, and output style, and Suzy adapts to how they work personally. Both layers are active at the same time.
Does Suzy's context update automatically? Yes. Knowledge base changes take effect immediately across every output. Suzy also updates its understanding of individual users continuously as they use the platform.
Which parts of Suzy use the knowledge base and brand context? Chat, Signals, and Stories Studio all draw on the knowledge base and brand settings. Chat responses, generated survey drafts, Signals feeds, and auto-generated decks are all shaped by what your team has loaded in.
Why can't I just use a general AI tool for this? General-purpose AI tools are only as context-aware as the prompt in front of them — whatever someone happens to include that session. Suzy's knowledge base is persistent and platform-wide, so your team's way of working is already built in before anyone types a word. It's the difference between briefing a contractor every time and working with someone who already knows how you operate.
