Our Methodology
Our methodology is built on a single premise: AI synthesis is only as good as the human signal it is grounded in. Every study begins with real participants whose responses calibrate the AI before any scaling occurs.
AI models are trained on vast amounts of general text. That training gives them language fluency and broad world knowledge, but it does not give them knowledge of how consumers in your specific category, in your specific market, actually think.
Before the AI synthesises anything, it is given verbatim responses, emotional language, decision rationale, and behavioural patterns from real people in the target market. The AI scales the volume. The humans define the signal.
Every study begins with a structured brief. We define the target population, geographic scope, research objectives, and the specific decisions the findings must support.
We select 200-300 real participants from a pre-screened global pool. Selection is behavioural first, with archetypes built around category engagement and decision context.
Each participant completes a multi-phase session combining quantitative survey work with open-ended depth questions that capture language, emotion, and motivation.
Human responses form the calibration layer before any AI synthesis begins. Verbatim responses are indexed and retrieved during synthesis.
Calibrated persona clusters extend the human signal across broader demographic and geographic diversity while preserving traceability back to the original data.
A held-out subset of real responses is compared against AI-generated responses across thematic overlap, depth, comprehensiveness, and qualitative alignment.
Every output is reviewed by a domain expert before delivery, with a methodology appendix suitable for research teams and procurement.
Every study includes an internal parity check before delivery. A held-out subset of real human responses is compared against AI-synthesised outputs across four weighted dimensions: thematic overlap, depth and specificity, comprehensiveness, and qualitative alignment.
Our panel participants are the specific human data that tells the AI how consumers in this category, in this market, at this moment, think and speak.
Purchase decisions are driven by behaviour, attitude, and context. We design every panel around how people actually engage with a category.
We route inference across multiple large language models to improve response diversity and reduce model-specific linguistic patterns.
The methodology is strongest for attitudinal and behavioural research: concept testing, brand perception, segmentation, message testing, and category mapping.
Precision about scope is a mark of methodological rigour. We are clear with every client about where our approach delivers its highest value, and where a different research method would serve better.
Crisis and trauma research, ultra-niche populations, behavioural observation, and legal or regulatory evidence require different research designs.