Behavioral research · AI Implementation
Turning a thirty-year research methodology into a working AI product.
The situation
A consumer-insights firm with a proprietary qualitative research methodology had spent three decades refining how they deliver behavioral insights to enterprise marketing teams. Their core deliverable was a written "blueprint" describing how a target audience subconsciously responds to a product or category. Every blueprint required a senior researcher to produce and a partner to deliver. The methodology was sharp. The delivery model didn't scale.
What we found
The written report limited how clients could use the research. Marketers wanted to ask follow-up questions, test creative against a blueprint, run different audience cuts. None of that was possible without booking another consulting hour. An interactive interface would let clients explore the same research without requesting a new report for each question.
A second problem sat underneath the first. Any AI layer dropped on top of the methodology had to preserve the firm's intellectual property. Protecting the proprietary material was a requirement for the architecture and for testing the customer-facing interface.
What we built
We began with the researchers’ existing process. The pipeline researchers already used to produce blueprints had manual steps that didn't require human judgment, and automating those gave the team back hours per blueprint and produced a clean dataset to build a customer-facing layer on top of.
The B2B product is a chat interface where marketers can query a blueprint directly. Underneath, a vector store handles retrieval and a custom instruction stack enforces the firm's methodology at the system level, isolated from anything the customer sees. Each client engagement can run multiple agents with distinct voices: an audience-persona agent, a brand-voice agent, and an evaluation agent that stays out of character. The architecture is platform-agnostic, with paths to run on different LLM providers as cost and customer requirements change.
What changed
Clients can ask follow-up questions through the interface, while researchers continue to develop the underlying methodology. The product keeps the proprietary instructions separate from the customer-facing experience.