UX Design Lab
Discuss a project
  1. WhenCall us when.01 of 08: When. Call us when.
  2. OutcomesWhat is different when the work is done.02 of 08: Outcomes. What is different when the work is done.
  3. HelpsHow UXDL helps.03 of 08: Helps. How UXDL helps.
  4. ProofGoverned knowledge for AI.04 of 08: Proof. Governed knowledge for AI.
  5. EngagementScoped to the problem, not a package.05 of 08: Engagement. Scoped to the problem, not a package.
  6. PavelWho you work with.06 of 08: Pavel. Who you work with.
  7. RelatedNearby services.07 of 08: Related. Nearby services.
  8. ContactStart a conversation.08 of 08: Contact. Start a conversation.

Part of: AI Product Design

AI UX / Trust Audit

Improve AI trust and adoption

Review autonomy, authorization, evidence, system state, failure, and human control across an AI or agentic workflow.

Discuss your AI experience

Call us when.

Start here when this is the situation in front of you.

  1. S/01

    Users cannot tell whether AI has acted.

  2. S/02

    Confidence or evidence is hidden.

  3. S/03

    Errors leave users without a clear recovery path.

  4. S/04

    Consequential actions lack an explicit authorization boundary.

What changes.

What is different when the work is done — things you can point to, not adjectives.

  1. Change 01

    Consequential actions have visible authorization points.

  2. Change 02

    Users can interrupt and recover from defined failure states.

  3. Change 03

    System state communicates what the AI prepared, proposed, and executed.

  4. Change 04

    Known failure scenarios have product-level controls.

How UXDL helps.

What the engagement actually produces.

  1. 01

    Trace decision and action paths.

  2. 02

    Review autonomy and authorization.

  3. 03

    Test failure and recovery.

  4. 04

    Evaluate evidence visibility and human oversight.

Proof.

Comparable work, with its provenance stated.

Proof · AI PRODUCT DESIGN · AI UX / Trust Audit

Governed knowledge for AI.

Context

The project defined permissions, provenance, freshness, evidence visibility, human responsibility, and approval boundaries around AI workflows. A limited pilot validated the core model.

Provenance

UX Design Lab client engagement.Read the case: Governed knowledge for AI. →
Architecture diagram: approved company sources feed a governed knowledge layer (ownership, permissions, freshness, provenance) exposed through MCP to multiple AI workflows, with a human approval boundary.
FIG. 03.1Governed knowledge layer between approved sources and AI experiences: ownership, permissions, provenance, MCP access.

Business pattern

AI becomes a governance problem when answers move faster than evidence, ownership, and accountability.

Pavel Bukengolts smiling and gesturing by a window overlooking a city skyline

Senior work stays senior.

You work directly with Pavel Bukengolts, UXDL founder and principal, on assessment, major decisions, and direction. Specialist collaborators join when the work requires additional expertise or capacity.

Recognition

  • ADPList 100

    Recognized among the top mentors on ADPList.

  • 2026 GNTC Tech Educator of the Year

    Finalist, Greater Nashville Technology Council.

  • UX Magazine contributor

    Published on product, accessibility and AI practice.

Put the AI workflow under a microscope before it carries more authority.

Bring the product, workflow, delivery problem, or decision. No polished brief required.

Prefer email? [email protected]

Project inquiry

All fields are required except timeline.