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Most teams overcomplicate audience building by starting at the wrong level of sophistication. This guide reframes the problem—showing how AI personas can evolve from quick prototypes to calibrated virtual panels that explore real conversational realities, not predict behavior.
When AI personas fail to predict consumer behavior, it’s not because the models are broken, it’s because we’re asking them to do something humans can’t even do for themselves. The real value isn’t in predicting what people *will* do, but in understanding how they *talk* about what they might — because that’s where decisions, and markets, are actually made.
A technical deep-dive showing how to automate audience uploads to AskRally using its API instead of manually editing JSON files. This guide walks developers and agencies through a reliable two-step process to create and update audiences programmatically across multiple AskRally accounts.
At the London Agentic AI Meetup, Mike Taylor showcased how to train an **LLM-as-a-Judge** and use it to optimize creative generative tasks through the **Evaluator–Optimizer pattern in DSPy**. His live demo built a *comedian AI* that learns to tell better jokes using **GEPA evolutionary optimization**, illustrating how to systematically create, evaluate, and improve AI programs.
Listen to this behind the scenes impromptu training between Mike and the Every.to team. Mike gives an introduction to the DSPY framework.
Good Innovation overcame the constraints of small budgets and limited sample sizes by integrating AskRally into their research process, scaling validation from a handful of interviews to 100+ participants. This hybrid approach has accelerated timelines, cut costs, and given UK charities more confident insights to guide innovation.
Give your AI personas personalized memory without the manual setup hassle.
Making AI assistants feel genuinely conversational isn't magic, it's methodical engineering.
Researchers often face the limitation that once a study ends, the insights are frozen in a static report. Study boosting solves this by transforming real survey data into living AI personas, allowing teams to extend research, test new scenarios, and localize messaging with authentic, data-grounded voices.
