AI can generate insights in seconds.
The harder problem is knowing whether they’re worth acting on.
In product discussions, teams rarely questioned what the AI concluded. They questioned how it got there. Without evidence, even accurate insights were hard to defend, and important decisions still meant reopening interviews and documents by hand. The challenge was never generating better summaries. It was designing an interface that made AI reasoning inspectable.
TraceFox is a product-discovery platform. I joined as the UX/UI designer, shaping its interface, flows and interaction logic. This is the investigation behind it — the questions, the rejected directions, and the decisions that stuck.
“I got as far as the part where you set up the workspace and I just closed the tab, it wanted a bunch of decisions I wasn’t ready to make yet. I never went back.”
Finding information stopped being the bottleneck.
Modern product teams have more research than ever — interviews, support conversations, analytics, product feedback, internal docs. The bottleneck moved. It’s no longer finding the information. It’s turning it into decisions everyone trusts.
Existing tools failed for opposite reasons.
Polished summaries, but little confidence. Impressive to read — impossible to defend in a review.
Evidence preserved, but only through constant manual upkeep — so the knowledge quietly dies.
The opportunity was to combine both: answers and the context behind them.
Why it mattered commercially: discovery only pays off when teams can act on research. An insight that can’t be defended in a roadmap review rarely changes the roadmap. So traceability wasn’t a UX nicety — it decided whether research influenced product direction at all.
The model wasn't the problem. The interaction model was.
My first instinct was to improve the AI’s outputs. But after mapping how teams actually worked, something became clear: users couldn’t inspect how conclusions were formed. Without that visibility, every AI-generated insight became just another opinion. That shifted the design question.
That single shift influenced every design decision that followed. Trust wasn’t going to come from a higher score. It would come from showing the work.
Four principles, not a feature list.
Rather than designing individual features, we set the rules the whole product had to obey.
Users should always be able to trace an insight back to its original source.
Generated knowledge starts as a draft until someone reviews the supporting evidence.
Confidence is communicated instead of hidden. Conflicting evidence is surfaced instead of ignored.
Documentation shouldn't require extra work. The repository grows as teams do their everyday jobs.
How much evidence belongs on screen?
I started from the opposite assumption: that transparency meant showing as much evidence as possible. My first prototypes attached citations to every generated sentence. In review sessions, people trusted the output more — but read it noticeably slower. That finding pushed me toward progressive disclosure. Step through the two directions I rejected and the one that shipped.
Evidence stayed one interaction away while preserving reading flow. Users stayed focused until they needed to verify — this became the foundation of the product.
Users abandon onboarding at the workspace-setup step.
trace to source →“…closed the tab, too many decisions I wasn’t ready to make.”
One pattern the whole product repeats.
Instead of automatically turning AI output into documentation, every claim passes through human review. The same four steps happen everywhere, so the mental model never changes.
AI proposes. A person reviews the evidence and approves. Only then does the shared repository update. There’s never a moment the system quietly decides something on its own.
Every insight is connected to its origin.
A claim is assembled in four layers, each grounded in the one below. Selecting any claim reveals the participant quote, interview metadata, supporting observations, confidence, and related evidence. Step it up one layer at a time.
A real sentence from a real interview. Nothing is inferred yet — this is the ground truth every layer above must point back to.
“I got as far as the workspace setup and I just closed the tab, too many decisions I wasn’t ready to make. I never went back.”
Rather than asking users to trust the AI, the interface gives them enough context to judge it themselves. Any reader can open a claim and land on the exact sentence a real person said.
Every element on a claim earns its place. Why each one exists:
Users abandon onboarding at the workspace-setup step.
- ① Confidence sits up front, not buried — it invites discussion instead of blind acceptance.
- ② Evidence stays one interaction away — transparency without slowing the read.
- ③ The claim stays editable — AI proposes, the person decides what the brief says.
- ④ The score recalculates automatically on conflict — the brief never goes stale in silence.
Research rarely produces one clear answer.
Participants disagree. Markets evolve. Evidence changes. Instead of hiding these inconsistencies, the product treats them as valuable signals. When new evidence conflicts with an existing claim, confidence decreases automatically and the contradictory sources are surfaced for review.
Onboarding drop-off is concentrated at workspace setup.
The claim remains, but its score falls and the conflicting source is named — nothing gets quietly overwritten.
A PM investigating a drop in onboarding completion.
- 01
Instead of rereading dozens of interviews, she asks TraceFox a question. The system proposes several possible explanations.
- 02
One hypothesis points to confusion during workspace setup. Before accepting it, she opens the supporting evidence.
- 03
Only after reviewing excerpts, timestamps and confidence does the insight become shared knowledge.
- 04 · weeks later
New interviews introduce conflicting evidence. The claim remains, but confidence changes automatically. Knowledge evolves without manual maintenance.
“…too many decisions I wasn’t ready to make.”
A few decisions shaped everything else.
AI positioned as a reasoning partner, not an answer engine.
The clearest signal came from how discussions changed. Product conversations stopped stalling on where an AI answer came from and moved to whether the evidence behind it was strong enough. That shift — from validating summaries to weighing evidence — was the outcome I was designing for.
- A claim is defended by opening its source, not re-reading the transcript.
- Contradictions surface on their own instead of being missed.
- The repository builds itself instead of needing upkeep.
- Shared briefs stay checkable — every reader can trace the reasoning.
AI products don’t become trustworthy by making fewer mistakes. They become trustworthy when people can see where a conclusion came from — and designing that moment became the real product.
The biggest lesson wasn't about AI. It was about information architecture.
Before this project I thought trust was mostly a function of model quality. Designing TraceFox changed that assumption. Users rarely know how accurate a model actually is — they judge whether they can independently verify its conclusions. So designing that verification layer mattered more than designing the summaries themselves. Explainability wasn’t a machine-learning problem; it was an information-architecture one. That insight still shapes how I approach AI products today.
TraceFox is in active development, heading toward launch from Munich in 2026. This case describes the product as designed and the thinking behind it, rather than adoption to date.