Read minds (simulated fMRI data, channeled to neuro-metrics)

MindReader is an AI brain activity simulation tool that takes user-submitted text and predicts how a human brain would respond region by region, offering granular neuroscience metrics. It belongs to the emerging category of emotional intelligence AI, serving marketers, sales professionals, and content creators who need empirical feedback on audience engagement. The core value is replacing guesswork with quantified neural reactions, enabling data-driven optimization before any real-world campaign launch. By modeling seven distinct cortical systems—from attention to language processing—MindReader provides a window into subconscious cognitive and emotional processes that traditional analytics cannot capture.

Marketers and sales leaders face a persistent challenge: they cannot directly know how their audience feels about content until it is too late. Traditional A/B testing takes weeks and provides only behavioral metrics like clicks and conversions, not the underlying cognitive drivers. MindReader solves this by directly simulating brain responses, showing exactly which parts of a message grab attention, feel personal, or demand mental effort. This matters because high engagement is not just about actions—it is about triggering the right neural responses that lead to memorability, persuasion, and purchase intent. With MindReader, users can preemptively identify weak spots in their messaging and iterate based on neural data rather than intuition alone.

The product's core feature is the 'Seven Systems' reading, which breaks down brain activity into seven distinct cortical networks: Attention, Personal, Effort, Gut, Memory, Social, and Language. Each system corresponds to known neural circuits, such as the dorsal attention network for focus or a network tied to autobiographical memory. The tool provides a numeric score for each system per content segment, allowing users to see not just if attention spiked, but why—due to novelty, personal stake, or emotional gut reaction. This granularity enables precise iterative tuning of headlines, calls-to-action, and narrative arcs. For example, a low Personal score might prompt rewording to include relatable examples, while a high Effort score could indicate overly complex language.

A standout capability is the word-by-word playback feature, as demonstrated in Maya's discovery call analysis. MindReader processes each word sequentially and assigns real-time scores for attention, personal resonance, and brain effort. When Maya mentioned 'listening to every call manually,' the attention score jumped to 0.89, signaling a high-focus moment. This level of detail shows exactly which phrases trigger cognitive engagement and which fall flat. Users can scrub through the timeline and see the cerebral impact of each sentence, making it a powerful tool for refining sales scripts, ad copy, or any persuasive text. The word-level reading also reveals cumulative effects—how a sequence builds emotional arcs over time.

MindReader v1

Key Features

Use Cases

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