You're in the readout. You've just shared the headline finding, and someone across the table asks the question every insights pro is waiting for: "Where did that come from?"
If your answer is an LLM or another AI tool, the insight is often met with skepticism on arrival.
That's why one of the most common concerns we hear from marketing and insights teams is "We can't trust AI-generated insights without a human checking them." It's a fair concern. Most AI tools are trained on the open internet, know nothing about your brand, and can't always tell you where their answers came from.
But the conclusion people draw from it is a myth: that AI insights are a black box you either take on faith or don't use at all. That's true of AI that wasn't built for research. Suzy was. And starting today, every answer she gives comes with the receipts.
Every answer in Suzy now shows its sources
Every chat response in Suzy now includes citations. Click the sources button on any response to see exactly what it was built from:
- Studies: All your primary research with Suzy
- Documents: Anything in your knowledge base
- Signals: Any category or trend intelligence from your signals feed
Citations work everywhere you chat: inside a project, from a signal, or on the homepage.
So you don't have to take an answer at face value. You can check it in two clicks, and when someone asks where it came from, you can show them.
Of course, citations only matter if the sources are worth citing. That comes down to two things a generic AI tool doesn't have.
Suzy was built by researchers, for anyone
Suzy was designed by people who've spent their careers running consumer research, so the methods a trained researcher would use are built into how she works. A brand manager gets the same rigor as an insights director, without needing to be one.
In practice, that means:
- Drafts start from your world. When Suzy writes a survey, a brand awareness question comes back listing your actual competitors, and a purchase question lists your real product lines.
- Findings are tied to your decision. Suzy weighs results against the objectives and hypotheses you set, so she surfaces what matters for the call you have to make, not just what's statistically interesting.
- She'll tell you when the evidence isn't there. If she doesn’t have enough data for you to confidently move forward, Suzy will point out where your gaps are.
Her context compounds, and so does the evidence she can cite
A generic AI tool starts from zero every time you open it. Suzy builds on everything you've given her:
- Your knowledge base: strategy docs, briefs, brand guidelines and past reports. Connect a Google Drive folder and it stays in sync automatically.
- Your research: every survey and Speaks study you've run, with the real consumer responses behind them.
- Your projects: objectives, studies, files, third-party data like purchase or tracker data, signals and saved chats for each initiative, all in one place.
- Signals: scored intelligence on your category, competitors and consumers.
- Your brand profile and memory: your competitors, product lines and segments, plus what Suzy learns about how you work. You can review, edit or clear anything she remembers.
Here's why that matters for trust. Say you're deciding which benefit to lead with in a relaunch, and you ask Suzy inside the project. Her answer might weigh last month's concept test, the positioning brief your agency sent over, and a signal on how the category is shifting, then cite all three, so you know exactly what her recommendation is based on. The more you build into Suzy, the more she can draw on, and the more of her answer you can trace, and more importantly, trust.
The bottom line
Being skeptical of AI is a good instinct. The answer isn't to avoid AI. It's to use AI that was made with expertise built in, that knows your business, and that shows its work.
Want to see Suzy in action? Book a demo to see AI insights you can stand behind.
