AI audience simulation by Uppercut Labs
What it is
A computational model of audience response.
Simul8 is our own AI simulation engine, built end-to-end at Uppercut Labs to model how an audience responds to a campaign, launch or crisis.
Each agent carries a distinct belief system, relationship network and memory of prior information. The model is grounded in real source documents, then run forward to observe how opinion, behaviour and sentiment evolve.
Under the hood
The working architecture.
Campaign simulation
Model how a target audience responds to a campaign message, visual language, or positioning before creative production begins. Identify which segments respond, which resist, and why.
Launch modelling
Simulate how an audience receives a new product, pricing change, or market entry. Test assumptions against a model grounded in real data rather than intuition.
Crisis response prediction
Understand how different audience segments are likely to respond to an incident, announcement, or controversy, and which communication approach is most likely to contain or repair it.
Segment behaviour analysis
Break audiences into distinct agent populations with different belief systems, information environments, and relationship networks. See how opinion travels between segments over time.
GraphRAG knowledge layer
Agent beliefs and knowledge are grounded in a knowledge graph built from real source documents, not invented by a model. Outputs are traceable to real evidence.
Scale modelling
Run simulations at realistic population scale, up to one million agents, so results reflect statistical properties of real audiences, not the noise of small samples.
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