Npire/
Benchmark
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Meet the Visual Perception Model.
VPMs are to UX what LLMs are to language.

A new class of AI, trained on human visual cognition — not the optical capabilities of the eye, but how the brain interprets and acts on what it sees. Benchmark is the first product built on a VPM: competitive UX audits, A/B testing, and redesign deliverables, all powered by a model that interprets your UI the way a real user will.

What you actually get

A working behavioral simulator. Feed it any persona, any task, any URL — it hands back a step-by-step play-by-play of what happens: which page the persona abandoned at, where they got lost, what specifically blocked them, and how long each step took.

The thesis

AI parallelism is exactly the structural advantage human-panel testing can’t touch. Personas × tasks × flows is a 3-dimensional matrix; humans can only sample one cell at a time, while an AI agent fleet can saturate the whole matrix in the same audit window.

The problem

Existing tools miss the point.

You can’t benchmark your UX against competitors using tools built to test your own product. Here’s what’s currently in the gap.

Human-panel testing tools Can't scale.

Human panels are inconsistent by design. No two participants are the same person. Competitor access is limited, expensive, and produces samples too small to compare statistically.

Browser-automation frameworks No perception.

Script automation interacts with code, not screens. It breaks when a button moves 10px. Zero capacity for judgment, confusion, or context. It tells you a form loaded — not whether anyone could fill it.

Session-replay tools Wrong subject.

Session replay tools require JavaScript installed on the product. You will never install it on a competitor's product. You see your users, never theirs.

Competitive-intelligence platforms Wrong dimension.

Competitive intelligence platforms track pricing, feature pages, and messaging. None of them have ever measured whether your competitor's quote flow is a 3-minute task or a 14-minute nightmare.

What makes Benchmark different

Three differentiators that define the category.

Powered by a Visual Perception Model (VPM).

Benchmark runs on a proprietary VPM — a new class of AI model. Where LLMs are trained on language, VPMs are trained on human visual cognition: not the optical capabilities of the eye, but how the brain interprets and acts on what it sees. We shape the persona; the VPM clones human perception for it.

vs. browser automation: code, not screen. vs. computer vision: pixel recognition, not meaning.

Statistically identical synthetic persona.

Each flow in your audit is tested by the exact same persona — same age, income, location, knowledge state, patience level, device, and behavioral rules. Run the audit again next quarter: same persona.

vs. human-panel testing: no two humans are the same.

The UI Clutter Index — a formula-driven scoring standard.

UCI is a defined, formula-driven friction score. Not a subjective rating. Not an AI vibe check. A calculated number based on element count, off-task ratio, and flow completion.

vs. vague usability scores from every other tool.

The model behind Benchmark

Visual Perception Model — VPM.

The VPM is a model of interpretation. Every other AI in this category processes a screen; the VPM processes how a brain processes a screen — the order it attends to elements, the inferences it draws about what each element is for, the friction it generates when a layout fights the user’s intent. Three things make that work: the research foundation it inherits, the corpus it’s trained against, and the persona-conditioning mechanism that runs at inference time.

Built on cognitive interference research.

Human visual cognition isn't passive recognition. It runs on parallel automatic and deliberate processing streams, and when a UI's visual hierarchy competes with a user's intent, the streams collide. Measurable interference results: slowed task completion, hesitation, missed actions, abandonment. The VPM operationalizes that interference at the UI level. The UI Clutter Index quantifies it.

Trained on meaning-making, not vision.

Computer vision recognizes objects: "this is a button." The VPM interprets comprehension: "this button is visually buried, so a real user wouldn't perceive it as the primary action." The training corpus is drawn from human-interpretation data — eye-tracking, task-completion telemetry, friction event tags, comprehension panels — not bounding boxes on pixels. Corpus composition and weighting scheme are proprietary.

Persona-shaped perception.

Each audit run conditions the VPM to a specific persona. Same model architecture; persona-conditioned weights for age, expertise, patience, knowledge state, device, and behavioral rules. The output isn't one generic perception of your flow — it's thousands of persona-specific perceptions in parallel, each consistent with how that persona would actually behave.

Read the full VPM brief

Research lineage, Aegis-warships origin, and the three pillars in depth — npire.net/vpm

A/B testing in a bottle

VPM-driven A/B testing, without the live deployment.

A/B tests today mean deploying the worse variant to half your users for weeks while you wait for statistical significance. The VPM runs the same experiment internally — both variants, with persona-modeled human responses — in minutes. Same outcome signal, none of the live exposure or the wait.

No live exposure.

The untested variant never touches a real user. Run the experiment in a sandbox; ship only the winner.

Minutes, not weeks.

A persona-modeled AI fleet runs the experiment in parallel. No statistical-significance wait. Results arrive before standup.

Per-persona signal.

See how each persona responds to A vs B independently — not just one blended conversion rate that hides who behaved how.

How it works

From brief to report.

01

Define personas and tasks.

Multiple templates per persona, multiple per task. We lock the matrix before any testing begins.

02

AI executes the matrix in parallel.

An agent fleet runs every cell — each persona on each task on each flow — three times, using only what the persona knows.

03

Human review gates.

Any uncertainty pauses the run. A human resolves it before scoring. No flow is penalized for edge cases.

04

UCI scoring and analysis.

Each stage scored. Each friction event logged. Cross-flow comparison built. Findings ranked by impact.

05

Deliverables packaged.

Slide deck, interactive flow diagram, written report, archived audit record. Ready to share with leadership.

The UI Clutter Index

A standard you can cite.

UCI = Total Elements × (1 + Off-Task Ratio)

A minimal flow scores under 15. A critical flow scores above 50. Unlike subjective usability ratings, UCI is formula-driven, reproducible, and directly comparable across audits, competitors, and time.

025507012Flow AMinimal23Flow BModerate34Flow CCluttered43Flow DCluttered56Flow ECriticalUCI score
Illustrative UCI scores. Lower is better.
What you get

Six deliverables in every audit.

Executive slide deck

(.pptx + PDF)

14–16 slides. Cover, context, per-flow summaries, UCI chart, findings, recommendations. Built to share with leadership without explanation.

Interactive flow diagram

(.html)

Stage-by-stage visual comparison of each flow. Stop markers, friction annotations, outcome chips, UCI scores. Opens in any browser.

Current vs Proposed comparison

(.html)

Side-by-side HTML mockups of each audited flow — your current screens next to the proposed redesign. UCI badges in screen headers, red/green change callout strips per screen, summary bar at the top.

Proposed redesign prototype

(.html)

A navigable redesigned version of every audited flow, automatically generated from the heuristics the audit applied. Each screen carries a UCI delta and the heuristics used. Click through it like the real product.

Written report

(.docx + PDF)

Detailed narrative findings with supporting evidence, full methodology disclosure, reproducibility notes, and a raw data appendix.

Audit record

(archived)

All run screenshots, UCI raw data, human review log, and persona file. Stored for twelve months. Available if findings are ever challenged.

Pricing

Per audit. No platform fees. No annual contract required.

Single audit

$3,500/ audit

One-time competitive snapshot. Ideal for pre-launch benchmarking or a board-level competitive review.

  • Up to 6 flows
  • Up to 3 personas
  • All 6 deliverables
  • 12-month audit record
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Most popular

Quarterly monitoring

$2,800/ audit

Same audit, run quarterly. Track how competitors' UX evolves. Includes trend comparison.

  • Everything in Single Audit
  • Trend delta vs. prior run
  • Persona version control
  • Priority scheduling
  • Billed quarterly
Start quarterly

Enterprise

Custom

Multiple verticals, custom personas, expanded competitive matrices, white-label deliverables, or API integration into your research stack.

  • Unlimited flows per audit
  • Multiple concurrent personas
  • White-label reports
  • Dedicated research lead
  • SLA-backed delivery
Contact
Add-on · any tier

VPM-driven A/B testing

Run paired-variant A/B (or A/B/n) simulations through the VPM. Per-persona signal, results in minutes, no live deployment.

$1,500
per variant pair
Add to audit

If your team has ever debated how a competitor’s onboarding actually compares to yours and ended the conversation with “I think it’s faster” — Benchmark exists to settle it.