A modelling product, organised around how analysts actually reason about a solve.
Mature enterprise tools accrete bespoke run-forms because they were never built around the user’s question. Solve Axis starts clean — with AI as connective tissue and a designed accountability layer, not a feature bolt-on.
Abstracted from real enterprise work · anonymised throughout · a fictional product.Four principles the whole product obeys.
Configuration is an expression of a model
Not a pile of bespoke run-forms. A few underlying dimensions organise every solve, so the user navigates by intent — not by hunting for the right named form.
Two kinds of confidence, never one
Solver optimality — defensible maths — and AI-selection confidence — advisory judgement — are shown apart. A single blended number is worse than nothing.
Override is a recorded decision
Pushing back means changing the question, not nudging sliders. Every choice — change or keep — is written to a sealed, signed, immutable account.
Show the boundary, don’t hide it
Some questions can’t be run at scale. The product renders where it ends — and names what’s a design problem versus a platform decision.
Ask → Review → Record.
One run, end to end — and the rule that holds throughout: nothing executes until a human has reviewed it.
Goal-Led Entry
Describe the decision in plain language. The AI proposes the right solve — with a recognizable anchor — and keeps the model’s dimensions backstage.
The Accountability Moment
Before anything runs: two confidences, the one load-bearing assumption pulled forward, and override expressed as changing the question.
The Decision Record
Run & record seals a tamper-evident artifact — assumptions, confidence basis, overrides and a human sign-off — exportable for audit.
Six real screens carry the argument.
Pulled straight from the design file. Click any screen to open it full-size — representative solves at different corners of the model, plus the wider platform they live in.
A model, then AI as its connective tissue.
The organising dimensions locate any solve, so the same surface absorbs the next run type the market invents. AI doesn’t sit beside the model — it expresses it: proposing a solve from a plain-language goal, and carrying a designed accountability layer that makes it safe to trust on a high-stakes decision.
Not every question can be run.
The same dimensions that organise a solve also map its cost. Some corners are cheap, some expensive, some beyond reach today — and the product says so, plotting where a question sits and offering the honest response.
Green and amber are a design problem. Red is a platform decision. Knowing which is which — and never blurring the two — is the product.
Four AI personas, run as concurrent agents.
Each persona reviewed the work twice — a pre-read round that shaped the brief, and a reaction round against the finished screens. Independent lenses; the verdicts moved.
This case study is the demo.
It’s a live demonstration of the AI Product Clarity Audit — the same lens applied to your product: where AI genuinely helps, where it’s risk theatre, and how design makes it trustworthy.