Defense-adjacent geospatial intelligence

The problem was never the design. It was that the product couldn't show its work.

A defense-adjacent geospatial intelligence team brought Bayesian Leaf in to fix onboarding for one product. They kept expanding the mandate, from that scoped project to standing product and design direction.

01·the diagnosis

The work: make the product legible enough to work self-serve as the platform opened to new kinds of people, and cut the manual onboarding the team carried for every new account.

The opaque system

Answers came out. How the system got there stayed hidden.

The client's analyst platform surfaced answers but hid its own reasoning. Someone new landed in a workspace with four competing workflow entry points on the welcome screen, data-source toggles and engineering debug views exposed in the interface, a tab bar that buried the answer, and a "search" that was really a chat box with no visible scope.

You could get an answer. You couldn't see how the system reached it, or steer it. The decisions were invisible.

✿The real problem was the gap between what people need and what they know how to ask.

Cleared artwork goes here when the client signs off.

Fig 01Four competing entry points, engineering debug views exposed in the interface.

The read

The instinct is to treat this as a design cleanup. It wasn't. Every complaint traced back to one thing: the product made decisions the person couldn't see. The work was to make those decisions legible.

✿A pile of usage complaints reduced to two problems nobody had isolated: does a new person ever find value, and when they do, does the workflow close.

02·made legible

Made legible

Pull the system's decisions to the surface.

The direction surfaced what the product was doing. Scope shown once, in dedicated widgets for location, time, and topic, instead of repeated everywhere. The reasoning moved into the workbench as one line that expands into the full step list. The answer became the default landing state. A guided onboarding layer taught the product's own anatomy: how it reasons, how to refine scope, where the evidence lives, how the map works. And a monitoring model let a person watch a search over time instead of re-running it by hand.

Confidence indicators and semantic grouping were part of the same move.

Confidence scores meant nothing to the people arriving. Percentages became semantic tiers traced to corroborating sources: the product is decision support, not the decision.

Less than five weeks in, this direction was already in front of the CTO and CEO.

✿Making the system's judgment visible, not decorative.

✿The monitoring concept came out of a working session with the product manager, weeks before anyone asked for it. Direction that ships months later is still direction.

The welcome screenFour competing entry points.

Made legibleThe answer as the default landing state.

03·shipped and adopted

Shipped and adopted

The direction didn't sit in a deck.

The prototype became the spec the engineering team rebuilt the product from. The welcome-screen cleanup shipped, and the guided onboarding was cleared to engineering.

Cleared artwork goes here when the client signs off.

Fig 02The prototype beside the spec engineering built from it.

04·the inversion

The CEO surfaced a run of individual UX problems using the product on a live task. Instead of triaging them as a bug list, Bayesian Leaf collapsed the whole pile into one diagnosis.

The product's modelChange your search and re-run it.

What analysts actually wantedFilter the results already in front of them.

One reframe turned a mess of complaints into a single, addressable direction. That is decision infrastructure made visible, in the room, in front of the person who runs the company.

The direction shipped and became how the product works.

A scoped onboarding became standing product and design direction.

The CEO began dogfooding the product on real work and bringing UX problems into the product conversation, and Bayesian Leaf was pulled into the recurring product sync. The relationship inverted: from "here's a project" to "help us decide where the product goes."

Week two produced the principles the team now evaluates decisions against: if a change served none of them, it was not a priority. Direction stopped being a meeting and became a standard.

✿I ruled out a redesign. Same screens, the decisions made visible.

✿I wrote these so the team could tell me no with a reason.

AltitudeWeek 4Direction to the CEO and CTO.

EngagementScoped → standingOne scoped project became standing product and design direction.

The outcome numbers aren't here yet. Post-change usage is on the client's analytics, and it lands here when it clears.

Bayesian Leaf didn't redesign screens. It took a system whose decisions were invisible and made them legible, so the team could see what the product was doing and steer it.

The one question

Can the people using your product see the decisions it's making for them?

If the answer is no, that's rarely a design problem.

The problem isn't design. It's decision infrastructure.

Let's talk

If your product is making decisions your people can't see, that's the problem worth fixing.

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Bayesian Leaf is the practice of Hew Suber.