Where AI actually helps, and where it doesn't
A working session with your research or design leadership. You bring your current process, your tooling and your constraints. You leave with a clear read on which tools are worth adopting, which are noise, where the real time savings sit for your team, and what to be careful about when the data is sensitive.
Useful when you're being asked to have an AI position and would rather it were an informed one. Runs remotely or in person, and we send a written summary afterwards so the conclusions survive the meeting.
Hands-on training for researchers and designers
We work on your live projects rather than toy examples, so your team finishes with methods they have already used once. It covers the practical craft — prompting for synthesis, checking AI output against the raw data, keeping the audit trail a regulated business needs — and a way to evaluate new tools as they keep arriving.
Best for in-house teams who have the research capability already and want to move faster without lowering their standards.
You have a question. We run the study and answer it.
Scoping, recruitment, moderation, analysis and a findings session your product team can act on the same week. Generative or evaluative, qualitative or mixed. Best suited to teams who need a decision-grade answer before the next planning cycle closes.
Every engagement is led by a senior researcher who stays on it from scoping through to findings. There is no handover to a junior team once the contract is signed.
Being specific about the AI part.
It is a fair question to ask, so here is the honest split.
What AI does
- Screening and scheduling participants
- Transcription, translation and tidying
- First-pass coding across dozens of sessions
- Surfacing patterns across past studies nobody has time to reread
- Drafting the findings deck from agreed themes
What stays with our researchers
- Deciding what question is actually worth asking
- Designing a study that can answer it
- Moderating — reading the room, following the thread
- Judging which patterns matter and which are noise
- The recommendation, and standing behind it