↳ Platform 02 // multimodal foundation model
Predicted neural response for text, audio, video and imagery.
The Adhishtanam Neural Engine maps text, audio, video and imagery to predicted cortical and subcortical activation, so a decision about a stimulus can be argued from a measurement instead of a meeting.
↳ What it returns
What the model returns.
The model returns a spatial map and a temporal curve. Both are inspectable: you can see which segment produced which prediction, and disagree with a specific frame rather than with an overall score.
Attention and salience
Where the response concentrates in the first seconds, and which moment loses the viewer.
Reward and friction
Predicted activity in reward-anticipation and negative-affect regions across a sequence.
Memory encoding
Which frames are likely to be recalled later, scored frame by frame rather than per asset.
Emotional valence
Direction and intensity of affect, with the segment boundaries that produce the shift.
Demographic and linguistic calibration
Response differs by language and population. Calibration keeps the prediction anchored to the audience actually being addressed, not a generic global panel.
Segment-level comparison
Compare cuts of the same asset and see where the response diverges, so an edit decision is traceable to a moment rather than a preference.
Exportable scorecard
Regions, segments and confidence travel together, so the result can be attached to a review, an approval or a research record.
↳ Architecture // stimulus to prediction
Encoders in, activation priors out.
Inputs are encoded into a shared representation, aligned in time, and projected onto regional targets learned from neuro-response recordings. Nothing about the pipeline requires the stimulus to leave your infrastructure.
Multimodal encoding
Text, audio, video and still imagery are encoded into a common representational space, preserving temporal order for moving stimuli.
Temporal alignment
Modalities are synchronised so a prediction can be attributed to a specific second of a sequence rather than to the asset as a whole.
Regional activation
The shared representation is projected onto cortical and subcortical targets learned from fMRI and EEG recordings.
Scorecard
Regional output is aggregated into the dimensions a decision-maker uses, with the underlying regional evidence kept attached.
On-premise inference
Weights and pipeline run inside the customer boundary. Pre-release creative and unpublished research material stay where they were produced.
↳ Method // evidence and limits
Method and known limits.
The engine is trained on high-fidelity fMRI and EEG neuro-response datasets. That grounding is the reason we can describe specific regions rather than a single engagement number, and the reason we are careful about what the output does not mean.
Haemodynamic signal fMRI BOLD
Localised changes in blood oxygenation give spatial resolution: which regions are involved in a response, and how strongly.
Electrical signal HIGH-DENSITY EEG
Millisecond-resolution event-related potentials give temporal resolution: when the response happens within a sequence.
Signal fusion CROSS-MODAL CORRELATION
The two recording types are correlated with model output so the spatial and temporal accounts agree, instead of one being reported in isolation.
The engine predicts aggregate neural response to a stimulus. It does not diagnose, treat or predict individual behaviour, and it is not a substitute for a clinical assessment, a panel study or a statutory review.
↳ Applied surface
The same model, delivered as a creative tool.
Wreltik packages the Neural Engine for advertising and short-form video teams. Upload a cut, receive a six-dimension scorecard, and compare edit variants before media spend is committed.