New Practice · 2026
As companies race to embed AI into their products, the biggest risk isn't a bad model, it's a bad experience. How you communicate uncertainty, handle errors, and earn user trust: those are design decisions. We help you get them right.
AI Trust Dimensions
Transparency
Most neglected
Error Communication
Often absent
User Control
Frequently missing
Explainability
Rarely designed
Consent Design
Mostly performative
Industry average scores across 50+ AI products assessed in 2024. Most companies have significant trust gaps before users notice them.
Why this matters
Every major product company is embedding AI. The models are getting better every month. But most companies are so focused on what the AI can do that they're not thinking about how users experience it.
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When an AI gets it wrong — and it will — does your user know? Can they correct it? Do they understand why it happened? Do they have control? These questions determine whether your AI product builds trust or destroys it.
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The EU AI Act, India's emerging AI regulations, and growing user scrutiny mean these aren't optional questions anymore. They're the design foundation every AI product needs.
01
The transparency gap
78% of users in a 2024 study couldn't tell when an AI feature was making a decision vs. a human. That's not a model problem — it's a design problem.
02
Error cascades
When AI fails silently, users trust results they shouldn't. When AI fails loudly, users abandon products they should trust. The difference is design.
03
Regulatory pressure
The EU AI Act requires explainability, human oversight, and accountability by design. India's AI framework is following. Early movers have an advantage.
04
The trust premium
Products with high AI trust scores see 40% better long-term retention. Users who trust an AI feature use it 3× more. Trust is revenue.
Our Offerings
Each offering is designed for a different stage of AI maturity — from pre-launch audit to ongoing governance.
01
AI Product Governance Audit
A comprehensive review of your existing AI product against responsible design principles. We deliver a trust report with prioritised, actionable recommendations, not just a checklist.
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Transparency and explainability review
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Consent flow and user control audit
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Error state and failure mode mapping
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Bias and fairness surface assessment
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Regulatory alignment check (EU AI Act, etc.)
03
AI Readiness Assessment
For companies exploring AI adoption, we assess your product, team, and UX readiness before you commit significant resources and deliver a prioritised implementation roadmap.
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Current product AI integration audit
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Team capability and readiness mapping
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Use case prioritisation framework
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Risk and trust impact analysis
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Phased implementation roadmap
02
Design for AI Sprint
A 3-week intensive engagement to design the human experience around your AI features responsibly, ideal for teams launching new AI capabilities or redesigning existing ones.
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AI interaction pattern design
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Uncertainty communication design
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Human-in-the-loop workflow design
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Feedback and correction flow design
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Responsible AI component library
04
Ongoing AI Governance Retainer
For companies shipping AI features regularly, we become your embedded responsible design team, reviewing new features, maintaining governance documentation, and keeping you ahead of regulation.
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Monthly feature review and sign-off
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Governance documentation maintenance
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Regulatory monitoring and briefing
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Team training and design principles
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Quarterly trust audit report
Our Principles
Principle 01
Transparency by default
Users should always know when they're interacting with AI, what data it's using, and how confident it is. This isn't a feature, it's a foundation.
Principle 04
Explainability over mystery
When an AI makes a recommendation or decision, users deserve to understand the reasoning, even if it's simplified. Black boxes erode trust.
Principle 02
Meaningful human control
Users must have the real ability to override, correct, and opt out of AI decisions, not just the appearance of control through buried settings.
Principle 05
Consent that means something
Consent flows should be clear, specific, and genuine, not dark patterns designed to extract maximum data with minimum awareness.
Principle 03
Graceful failure design
AI will make mistakes. The measure of a responsible AI product is how well it communicates, recovers from, and learns from those failures.
Principle 06
Accountability in the design
Who is responsible when the AI is wrong? Good AI governance design makes this clear to users, to regulators, and to the company.
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