Tailorable services,
fully flexible to your needs.

I

Start Up Discovery Sprint

Rigorous first-round responsible AI user research for teams who need to validate before they build.

Early-stage founders often build for months before discovering their assumptions about users were wrong. Academic-grade discovery research before you build saves time, money, and the risk of designing for the wrong person entirely — but most options are too expensive, too slow, or too shallow.

II

Inclusive User Experience Discovery

Test your AI product with the users who reveal what standard testing misses.

Standard usability testing does research with digitally confident, articulate adults. It ignores crucial diverse voices — neurodivergent, disabled, BAME, LGBTQI+ populations and many others — validating the experience of the minority and embedding exclusion into your product. Listening to people who are marginalised reveals failure modes in trust, comprehension, and fairness that standard testing never surfaces.

III

Responsible AI Co-design Sprint

Design your AI product with the people it will affect — not just for them.

Most AI product design happens in a room of engineers and designers — not the people the product is actually for. This embeds assumptions, blind spots, and exclusions at scale. Participatory design isn't just more ethical; it produces products that work better, fail less, and earn trust faster.

IV

Human-Centred AI Discovery

Understand who you're building for — before you build anything.

Most AI teams start with what the technology can do, not what people actually need (or don't need) from it. Standard user research wasn't built for AI's distinctive challenges: opacity, unpredictability, trust, and the risk of harm at scale. The result is products that are impressive in demos and ignored — or harmful — in the real world.

I

AI Readiness & Impact Scan

Understand who will be affected — and how — before you embed AI.

You're planning to adopt AI but haven't yet mapped who it will affect and where the real risks lie. Without this groundwork, adoption creates human problems that need critical and responsible examination — and that are expensive to fix later.

II

Stakeholder AI Experience Study

Find out what it's actually like to use AI tools as part of your team's daily work.

Your organisation has deployed AI tools, but you don't know how stakeholders, employees or users genuinely experience them — the trust issues, the workarounds, the unfair outcomes. Surveys miss what people won't say to a manager. The result is low adoption, hidden friction, and legal exposure you can't see.

III

Community AI Perceptions Study

Find out what the communities you serve actually think and feel about AI, in creative ways.

Most organisations consult communities after decisions are made — presenting plans and inviting reaction. That's not research; it's notification. When it comes to AI, the communities most likely to be affected are also the least likely to be asked. Left unexamined, that gap becomes a reputational risk, a democratic failure, or both.

IV

Ongoing Research Partnerships

Embedded research expertise for teams engaging with AI responsibly over the long term.

AI products don't stand still — they learn, adapt, and affect people in new ways over time. A one-off research sprint gives you a snapshot. Responsible AI futures require sustained research: continuous monitoring, early identification of emerging harms, and human-centred input at every product milestone.