AI & Automation
Faster, deeper, more accessible research.
AI is why a mid-market company can now afford methods that used to be reserved for companies fifty times its size. It does the heavy lifting — transcription, coding, pattern detection, monitoring, reporting — at a speed and cost no team of humans can match.
It does not do the thinking. Every insight on every project comes from a senior researcher working on pre-validated, quality data. The technology amplifies the researcher. It never replaces them, and we will not pretend otherwise — because a wrong answer delivered fast is still a wrong answer.
What we help with
Two kinds of work, and most clients end up buying both. Advisory sets the rules; capability does the research. Each card says which it is.
Where AI stops. AI does transcription, coding, classification and monitoring. Design, interpretation and the recommendation stay with a senior researcher who signs the conclusion and can defend it. That is not caution — it is where the value is.
AI readiness audit
Shadow AI is the norm — tools nobody approved, on data nobody classified. We map how research actually gets done in your organisation, then say what to automate, what to assist, and what must stay human.
Advisory · 3–5 weeks
AI governance for research data
What data may enter which system, what respondents are told, what a human must sign off, and what happens when something goes wrong. Grounded in ESOMAR guidance and the data-protection regimes that actually apply to you.
Advisory · 4–8 weeks or retained
Dormant-data diagnostic
Know what you already know. A fixed-scope audit of the brand, marketing, CRM, social and past-study data you already hold, each asset mapped to a live decision, with a prioritised activation roadmap.
Advisory · fixed scope
AI-accelerated research
Qualitative and quantitative studies with AI embedded where it genuinely helps — adaptive interviewing, open-end coding, multilingual synthesis — and nowhere it does not. Design and interpretation stay with senior researchers.
Capability · per study
Data integrity & respondent verification
Synthetic respondents and machine-written open-ends clear checks that were built to catch humans behaving badly, not machines behaving plausibly. We audit your sample sources, detect machine-generated patterns, and design the architecture that keeps them out.
Capability · project or retained
B2B signal & buying-group intelligence
Your ideal customer profile rebuilt from interviews with buyers, losers and non-buyers, combined with observable market signal — then the buying committee mapped by role: who decides, who blocks, who holds a veto nobody records.
Capability · project or subscription
AI visibility & machine-audience research
Your next buyer may never read your site — their assistant will. Prompt-level tracking of how your brand, your competitors and your category are represented across the major assistants, in the languages your markets actually use.
Capability · audit + tracker
AI training data & managed annotation
Annotation schemes, expert annotator recruitment, inter-rater reliability and an ethically contracted workforce — for teams whose model quality is really a data-quality problem. Spanish and Portuguese without the region-blind flattening.
Capability · per programme
UX research for AI products
Testing an AI product is not testing a website: the interface is non-deterministic and users over-trust confident errors. Prompt-flow analysis, failure and recovery observation, and trust calibration measured over repeated use.
Capability · per study
Category & competitor radar
Continuous monitoring of your category, competitors and customer sentiment across social, news, forums and review sites — defined sources, a taxonomy we design, threshold alerts, and a monthly senior read of what changed.
Capability · continuous
Voice of customer analysis
Verbatims, reviews, support tickets and social posts pulled into one place and coded against a taxonomy we design for your category, with the themes and their movement reported monthly.
Capability · continuous
Predictive segmentation
Behavioural and first-party data modelled into segments that update as behaviour changes — rather than a segmentation that is accurate the day it is delivered and stale a quarter later.
Capability · per study
Reputation & risk monitoring
Language screening across owned and earned conversation for complaint drivers, safety issues and reputational signals, escalated to a named person on your team in minutes rather than at the next report.
Capability · continuous
Measurement dashboards
Results from multiple sources consolidated into one live view with the anomalies flagged, so the monthly meeting is a discussion of what to do rather than an assembly exercise.
Capability · continuous
Book a call with a senior researcher
Tell us the decision you're facing. A senior researcher will help you diagnose it — no sales pitch, and no obligation to run a study.
Questions we get
About AI in research
Does AI replace the researcher?
No. AI does transcription, coding, classification and monitoring. Every insight comes from a senior researcher working on validated data. A wrong answer delivered fast is still a wrong answer.
Is our data used to train AI models?
No. Client materials stay in a secure, controlled environment and are never used to train public models.
Why do so many AI projects fail?
The failure modes are well documented: automating a messy process instead of fixing it, no written success metric before the build, too many workflows at once, and a poor data foundation. We start with one workflow, define the metric first, and establish the data foundation before building.
We already have AI tools. What would you add?
Most organisations have them. Almost none can say who approved them, what data goes into them, or who is accountable when an output is wrong. Tools are an input to a capability, not the capability itself. We design the system the tools sit inside.
Is this just research with extra steps?
It is research with fewer steps and better evidence. The methodology does not change. The elapsed time and the scale you can reach do.
Why a research firm and not an AI consultancy?
Because the failures in AI-assisted research are research failures — sampling error, unrepresentative data, findings that sound right and are not. Diagnosing those takes people who have spent a career doing it. An AI consultancy will build you a fast pipeline for producing confident nonsense.
Are the percentages on this page your results?
No, and they are labelled that way on purpose. Any range shown is an outcome reported for this class of automation across the industry, carrying its source. We have not measured it on your account and will not claim it until we have. Everywhere on this site that we quote our own numbers — 339 studies, 180 tracking programmes — they come from our studies ledger and say so.
Start with what you already have.
Most companies are sitting on data they have never turned into a decision. A senior researcher will help you find out what is in it.
