Multimodal AI neuromarketing research

We build predictive digital twins of target consumers.

The Neurobusiness Corporation helps B2C organizations move from one-time research to a continuously improving consumer intelligence system, grounded in their own EEG, eye-tracking, GSR, and ECG research data.

5Neuromarketing studies

50Participants per study

250Participants in total

1Target consumer profile

1Decision category per model

01 · The company

From human response data to a usable consumer digital twin.

Traditional neuromarketing often ends with a report. Our model is designed to learn across five consistent studies, identify stable cross-project patterns, and create a predictive digital twin of the average target consumer within one clearly defined decision category.

What the digital twin is

A validated, population-level model of the average cognitive, attentional, emotional, and physiological response patterns found within a precisely defined target consumer segment.

What it is not

It is not a replica of one person, does not identify individual participants, and is not intended to predict behavior outside the consumer profile and decision category in which it was trained.

AI neuromarketing is not about replacing human judgment. It supports judgment with biological evidence, structured learning, and predictive clarity.

02 · What we measure

How people see, feel, process, remember, and decide.

Neuromarketing does not read minds. It measures biological signals that reflect how the brain and body respond in real time, often before people can explain their reactions in a survey or interview.

Visual attentionWhere people look, what they notice first, and what they ignore.

Emotional arousalThe intensity and timing of physiological response, not simply like or dislike.

Cognitive effortHow hard the brain appears to work while processing information or completing a task.

Memory-related signalsPatterns associated with what may be encoded and remembered later.

Confidence and hesitationWhether a stimulus supports action or creates friction, uncertainty, or doubt.

03 · Multimodal method

Four biometric views. Two randomized groups. One integrated picture.

No single signal tells the full story. Multimodal measurement improves interpretation by showing where signals converge, where they diverge, and which patterns remain stable across studies.

EEG

Neural activity associated with attention, cognitive processing, engagement, memory-related dynamics, and mental effort.

Eye tracking

Gaze sequence, fixation, visual priority, navigation, attention, pupil response, and areas of interest.

GSR

Skin conductance changes associated with physiological arousal and the intensity of emotional response.

ECG

Heart-rate and variability patterns that add context to arousal, regulation, engagement, and moment-to-moment response.

Research Group 1

Eye tracking + GSR + ECG

Focused on visual attention, emotional intensity, physiological engagement, gaze behavior, and decision interaction with the stimulus.

Research Group 2

EEG + GSR + ECG

Focused on neural processing, motivation, memory-related patterns, cognitive effort, arousal, and response regulation.

04 · Decision categories

Each digital twin is trained for one kind of business decision.

Advertising, packaging, websites, brand messaging, and comparative concept decisions engage different cognitive and emotional processes. For scientific consistency, each category is modeled separately.

Advertising & campaign effectiveness

Attention capture, emotional impact, engagement consistency, message fatigue, and decision readiness before media spend is committed.

Product & packaging design

Visual scanning, clarity, trust, shelf stand-out, cognitive load, emotional confidence, and purchase hesitation at the point of choice.

Digital experience & website UX

Navigation flow, visual priority, emotional response, cognitive effort, friction, hesitation, confidence, and completion behavior.

Brand & message effectiveness

Emotional resonance, trust, credibility, message coherence, identity alignment, memory-related signals, and confusion risk.

Comparative concept testing

Relative attention, emotional contrast, preference formation, decision readiness, salience, and performance differences across options.

05 · Digital twin build

Five studies become one predictive consumer model.

The complete build requires five projects with 50 participants each. Every study must use the same target consumer profile and remain inside the same decision category so the model learns comparable patterns rather than mixing unrelated behavior.

01 · Define

Lock the scope

Define the target consumer, business question, decision category, stimuli, outcomes, and research guardrails.

02 · Measure

Run five studies

Recruit and test 50 qualified participants per study using controlled multimodal neuromarketing protocols.

03 · Connect

Synchronize signals

Align EEG, gaze, GSR, and ECG features to the exact moments and stimuli participants experience.

04 · Validate

Find stable patterns

Separate repeatable cross-study patterns from noise, assess bias, and validate the model’s use boundaries.

05 · Deploy

Activate the twin

Make the target-consumer digital twin available through a secure AI client portal for category-specific prediction.

06 · Three-year vision

Insight compounds into prediction, efficiency, and long-term advantage.

The greatest value is not only what a study explains today. It is what the growing evidence base enables the client to predict tomorrow, at lower cost and with greater confidence.

Year 1

Foundation and learning

Five studies with 250 participants
Multimodal biometric measurement
Neurosegmentation and pattern discovery
Training the first category-specific digital twin

Value: a reliable evidence base showing how the defined consumer segment responds within one decision category.

Year 2

Prediction and efficiency

Targeted refresh studies
Model recalibration and accuracy checks
Faster evaluation of new stimuli
Fewer participants needed for selected validation tasks

Value: predictive insights can be applied to new stimuli in the same category before full-scale testing.

Year 3

Scale and strategic advantage

Light refresh studies
Deeper recalibration when needed
Detection of emerging behavior shifts
Long-term decision support across initiatives

Value: a mature intelligence system supporting faster launches, prioritization, and earlier risk detection.

07 · AI client portal

A secure control panel built on the client’s own research.

After five completed studies unlock the digital twin, clients can use a private portal to evaluate new stimuli, ask strategy questions, and receive structured predictive reports grounded in their own biometric research.

Upload new stimuli

Upload advertising creatives, campaign videos, digital experiences, product concepts, or packaging designs for evaluation against the trained model.

Chat with a strategy assistant

Ask where attention drops, which emotional response dominates, whether a concept creates confidence or hesitation, and how specific elements could be optimized.

Receive predictive reports

Outputs may include emotional response prediction, EEG-informed engagement scoring, GSR-based arousal modeling, and gaze-driven focus mapping.

Important scope boundary: predictive outputs remain limited to the target consumer profile and decision category used to train the model. A new profile or category requires a separate research track.

08 · Research partnerships

Long-term research partnerships, not disconnected one-off studies.

Every project includes study design, stimulus preparation, participant recruitment, multimodal biometric collection, randomized assignment, data processing, quality control, and an executive-ready report with recommendations.

Six-month partnership

Two projects

$110,000

50 participants per project
100 participants in total
Multimodal biometric measurement
Two executive insight reports
No predictive digital twin yet

Best for campaign validation, product design checks, or early-stage decision support.

Digital twin buildTwelve-month partnership

Five projects

$260,000

50 participants per project
250 participants in total
One target consumer profile
One decision category
Cross-project pattern analysis
Predictive consumer digital twin

Unlocks predictive insights in the client portal starting in Year 2 at an additional standard monthly fee.

Three-year strategic partnership

Eight projects

$420,000

Year 1: five-project digital twin build
Year 2: one 50-participant refresh study
Year 3: two 50-participant studies
400 participants in total
Model maintenance and recalibration
Lowest cost per insight over time

Designed for organizations building a long-term predictive consumer intelligence asset.

Customized enterprise pricing is available for organizations managing multiple brands, consumer profiles, or decision categories.

09 · Build vs. partner

Predictive intelligence without years of internal buildout.

An internal capability typically requires senior neuroscience expertise, AI and machine-learning engineering, research operations, biometric hardware, data infrastructure, participant recruitment, compliance, and continuous model updates.

Category	Internal neuromarketing team	Partnering with us
Time to first insight	12-24 months	4-6 weeks
Upfront investment	High	None
Annual fixed cost	Very high	Predictable
Talent and hiring risk	High	Included
Biometric hardware	Capital expense	Included
Participant recruitment	Internal burden	Included
AI model development	Slow and internal	Continuous
Predictive intelligence	Rare	Core capability
Scalability	Limited by headcount	Flexible
Long-term risk	High	Lower

$2.916M

Illustrative three-year internal-team cost using four hires at an average $180,000 salary and a 1.35 overhead multiplier.

$2.496M

Illustrative cost difference compared with the $420,000 three-year strategic partnership. This does not include hiring delays, turnover, opportunity cost, or technical debt.

10 · ROI and value

Research becomes a reusable decision infrastructure.

Over time, organizations can benefit from fewer failed launches, faster decisions, less internal friction, clearer justification for spend, stronger alignment across teams, reduced testing redundancy, and lower cost per insight.

Illustrative practical example

$2,000,000 paid-media spend
$50 baseline cost per acquisition
2.0% baseline conversion rate
Conversion improves to 2.4%
CPA decreases to $42
About 7,600 additional customers without increasing spend
More than $900,000 in additional revenue

This is a hypothetical illustration, not a guaranteed outcome.

Illustrative three-year model

$2,776,877 incremental gross profit
$120,000 in testing savings
$420,000 total partnership investment
$2,476,877 estimated net gain
589.7% estimated three-year ROI

Actual results vary by category, creative quality, market conditions, media execution, and the assumptions entered.

11 · Global research hubs

Macro-regional research coverage with standardized execution.

The company is headquartered in Los Angeles. Regional hubs act as proxies for broader macro-regions, allowing culturally relevant recruitment while maintaining controlled research conditions in private professional business centers.

North America

Los Angeles, USA

United States and Canada; urban, digitally fluent, multicultural North American audiences.

8383 Wilshire Blvd., Suite 800
Beverly Hills, CA 90211

Western Europe

London, UK

United Kingdom, Germany, France, Netherlands, and Nordic countries.

Berkeley Square House
Berkeley Square
London W1J 6BD

Southern Europe & Mediterranean

Barcelona, Spain

Spain, Italy, Greece, and Portugal; emotionally expressive and narrative-driven audiences.

Avenida Diagonal 131
Barcelona 08018

East Asia

Seoul, South Korea

South Korea, Japan, Taiwan, and parts of China; digitally advanced audiences with strong visual processing.

Trade Tower, 27th and 30th Floor
511 Young Dong St., Gangnam-gu
Seoul 06164

Middle East & North Africa

Dubai, UAE

UAE, Saudi Arabia, Qatar, Egypt, and Morocco; culturally diverse MENA audiences.

Boulevard Plaza, Tower 1, Level 9
Sheikh Mohammed Bin Rashid Blvd
Dubai

Latin America

Mexico City, Mexico

Mexico, Brazil, Colombia, Argentina, and Chile; socially oriented and emotionally expressive audiences.

17th Floor, Torre Magenta
Paseo de la Reforma 284
Ciudad de México CP 06600

Sub-Saharan Africa

Nairobi, Kenya

Kenya, Nigeria, Ghana, and South Africa; rapidly growing digital adoption and strong narrative engagement.

Westlands Road
14th Floor, Global Trade Centre
Nairobi 54102

South Asia

Lahore, Pakistan

Pakistan, India, Sri Lanka, and Bangladesh; highly engaged and narrative-oriented audiences.

9th Floor, Tricon Corporate Centre
73 Jail Road, Gulberg
Lahore 54000

12 · Ownership and ethics

Human-centered research with clear data boundaries.

The platform is designed around informed consent, anonymization, aggregate analysis, client confidentiality, scientific validity, and transparent boundaries on what the model can claim.

Client ownership

Clients retain ownership of their study outputs and reports. One client’s proprietary research data is not sold, shared, or blended into another client’s digital twin.

Proprietary methods

Research methods, model architecture, analytical frameworks, quality-control systems, and the underlying platform remain proprietary to The Neurobusiness Corporation.

Participant protection

Studies use informed consent, privacy protections, anonymization, and aggregate analysis. The digital twin represents segment-level patterns, not identifiable individuals.

Validated use boundaries

Predictions are interpreted only inside the consumer profile, decision category, and conditions used to build and validate the model.

Applied AI engineering and automation division

Custom AI systems, engineered around your operations.

IntelliForge.AI is the applied AI engineering and automation service line of The Neurobusiness Corporation. It designs practical, governed AI systems for businesses, nonprofits, research teams, healthcare-adjacent organizations, enterprises, and public-sector projects. The goal is to improve operations while keeping people in control of important decisions.

Agentic workflow architecture

Custom systems for outreach, CRM, lead intake, personalized drafting, response classification, meeting preparation, follow-up tracking, grants, research operations, analytics, executive reporting, and operational dashboards.

Business and Google Workspace automation

Workflow concepts and integrations across Gmail, Drive, Sheets, Forms, and Calendar, supported by approval queues, operational state, audit history, source evidence, and clearly defined human review points.

Analytics and website intelligence

GA4, GTM, Google Ads, backend reconciliation, Firestore, BigQuery, dashboard-ready reporting, page discovery, quality assurance, SEO preservation, accessibility checks, stale-content detection, and draft-first WordPress updates.

Research, grants, and monitoring

Opportunity discovery, eligibility review, evidence mapping, literature support, readiness checklists, research intelligence, cybersecurity monitoring concepts, operational alerts, and decision-ready summaries.

Custom AI development

Bespoke models, assistants, applications, and automations designed around a specific operational problem, existing systems, data boundaries, and measurable outcomes.

AI software and subscription services

Where appropriate, selected solutions can be deployed as managed software or subscription-based services so organizations can use specialized AI capabilities without maintaining a full internal build team.

Enterprise and public-sector systems

Governed decision-support and workflow systems for larger organizations, infrastructure programs, research programs, and complex multi-team operations.

Medical neurotechnology and BCI engineering

IntelliForge.AI can engineer and deploy research-grade and production neurotechnology systems, including brain-computer interfaces, neural and biosignal processing pipelines, real-time inference, device-to-cloud integrations, edge AI, multimodal physiological data fusion, and researcher or clinician-facing intelligence systems.

Neuromorphic AI and advanced models

Custom neurotechnology projects can incorporate neuromorphic AI, spiking neural network concepts, adaptive decoding, advanced time-series models, predictive and multimodal AI, signal classification, anomaly detection, and other specialized models designed around the scientific or operational use case.

Medical and neurotechnology deployment: research, clinical, and production pathways are scoped around appropriate verification and validation, privacy, cybersecurity, quality controls, human oversight, and applicable regulatory requirements. Selected application areas also include energy and smart infrastructure, finance and risk management, healthcare and biotech, neurotechnology, smart agriculture, aerospace and defense operations, retail and consumer intelligence, nonprofits, research organizations, and government programs.

Human control and governance

Human approval before consequential actions
Privacy-aware data models and access boundaries
Audit trails, source evidence, and approval history
Monitoring, exception handling, and rollback planning
Clear limits on what the AI may decide or execute

Technical focus

Python, JavaScript, Bash, HTML, AI automation, prompt engineering, agentic workflows, Google Cloud, Firestore, BigQuery, Google Workspace, GA4, GTM, WordPress, CRM systems, neuromorphic AI, BCI architecture, biosignal processing, multimodal time-series modeling, research intelligence, and workflow documentation.

Every IntelliForge.AI engagement is a custom project.

Pricing varies based on scope, integrations, data requirements, model complexity, security, governance, deployment, and ongoing support. Project details are reviewed before a tailored proposal is prepared.

Email IntelliForge.AI Call +1 818 482 1727

13 · About the owner

Scientific thinking, applied AI engineering, and business strategy in one practice.

Arifa Kokab leads both The Neurobusiness Corporation and IntelliForge.AI, connecting neuroscience, artificial intelligence, research operations, and business strategy to build systems that are practical, responsible, and designed for real organizational use.

Arifa Kokab

Scientific Founder and CEO · Computational Neuroscience and Neuromorphic AI Engineer

Arifa works at the intersection of computational neuroscience, neuromorphic AI, biomedical neuroscience, and business strategy. Her credentials include an M.Sc.Eng. in Applied Artificial Intelligence from the University of San Diego, completed with a 4.0 GPA; an Executive MBA in Business Analytics from Hult International Business School with Gold Medal honors; and graduate training in Biomedical Neuroscience through the McKnight Brain Institute at the University of Florida.

She also serves as Head of the AI Division at the Society for Brain Mapping and Therapeutics, while leading The Neurobusiness Corporation's predictive consumer digital twin work and IntelliForge.AI's custom AI engineering practice.

Visit ArifaKokab.com Connect on LinkedIn

What she builds

Arifa's work connects brain-inspired computing, applied AI engineering, and practical organizational strategy. Her broader technical focus includes neuromorphic AI, brain-computer interfaces, computational neuroscience, digital twin neuromarketing, agentic workflow systems, and ethical AI design.

Through The Neurobusiness Corporation, she develops consumer intelligence models grounded in real neuromarketing data. Through IntelliForge.AI, she designs custom AI systems and automation for organizations that need intelligent operations while retaining human oversight over important decisions.

The most meaningful technology does more than automate. It amplifies human understanding.

14 · Frequently asked questions

What organizations usually ask before getting started.

What exactly are you measuring?

We measure visual attention, physiological arousal, cognitive effort, memory-related signals, engagement, confidence, hesitation, and response timing using EEG, eye tracking, GSR, and ECG.

Why not rely only on surveys or focus groups?

Surveys and interviews capture conscious explanations. Biometric methods capture real-time response processes that may happen before a person can clearly explain them. We view the methods as complementary, not mutually exclusive.

Why use two different research groups?

Eye tracking and EEG can each require different study conditions. Randomized groups allow each modality to be collected well while GSR and ECG provide shared physiological context across both groups.

Why is the minimum project size 50 participants?

Fifty participants provides a practical balance between feasibility, variation, reliability, quality control, and the ability to detect patterns within a defined consumer profile.

Why are 250 participants required for the digital twin?

The model needs five separate studies and enough variation across 250 total participants to learn stable cross-project patterns rather than overfitting to one small study or one set of stimuli.

Can different decision categories be mixed in one model?

No. Advertising, packaging, digital experience, brand messaging, and comparative testing are trained separately because each engages different cognitive and emotional processes.

What is neurosegmentation?

Neurosegmentation identifies meaningful cognitive and emotional response patterns within the defined audience instead of relying only on broad demographic averages.

Do you replace traditional consumer profiles?

No. The digital twin strengthens a business-defined target consumer profile with measured behavioral and biometric response patterns.

What happens after predictive modeling is unlocked?

The client can upload new stimuli, ask questions through a strategy assistant, and receive predictive reports grounded in the five completed studies. Portal access begins in Year 2 at an additional monthly fee.

Are fewer participants needed in later years?

Selected refresh and validation studies may use fewer participants because the model already has an established evidence base. The exact sample depends on the question, category, and required confidence.

How often should refresh studies be conducted?

Annual or semiannual refresh studies may be appropriate depending on how quickly the category, consumer behavior, culture, brand, or market conditions change.

How long does a typical project take?

A typical project can move from kickoff to first insight in approximately four to six weeks, depending on scope, recruitment requirements, stimulus readiness, and research complexity.

What do clients receive?

Study design, participant recruitment, multimodal biometric data collection, quality control, analysis, cross-project pattern learning when applicable, and executive-ready insight reports with recommendations.

Who owns the data and insights?

Clients own their study outputs and reports. Our research methods, analytical frameworks, model systems, and platform remain proprietary.

Do you guarantee marketing performance?

No. We reduce uncertainty and improve evidence quality, but market outcomes also depend on media execution, pricing, competition, creative quality, distribution, and other conditions outside the model.

Is this suitable for small or mid-sized brands?

The model is best suited to organizations making high-impact marketing or product decisions and able to support a structured multi-study research commitment. Smaller brands may begin with the six-month option.

Can agencies or internal teams work with you?

Yes. We can operate as the neuroscience and predictive intelligence layer alongside internal marketing, insights, product, analytics, and agency teams.

Why does this require a long-term commitment?

Predictive value comes from consistent data accumulated over multiple projects. A single study can answer a question, but five aligned studies are required to build the category-specific digital twin.

Q3 2026 fully booked

Q4 partnership spots are opening soon.

Stay tuned as we complete our platform migration and announce new Q4 2026 availability. Follow the company for launch updates, research openings, and the next version of our client experience. IntelliForge.AI custom project inquiries are reviewed separately by scope.

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