Built by Ishaan Goel
The first brand intelligence platform built on cortical neuroscience. See your brand through 200 simulated brains — before you spend a dollar on media.
Nemo uses Meta FAIR's TRIBE v2 foundation model to generate fMRI-grade BOLD signal predictions across 20,484 cortical vertices for any uploaded brand video. It runs those predictions through 200 AI consumer personas and surfaces a deep neuroscience report telling you exactly which brain regions engaged, when they peaked, how well the message encoded into memory, and where attention dropped.
Upload a video. Within seconds, Nemo:
- Encodes the video into brain space using TRIBE v2 — a tri-modal (video + audio + language) foundation model trained on 720 subjects and 1,000+ hours of fMRI data from Meta FAIR.
- Streams live cortical activations over WebSocket — a 3D brain mesh lights up in real-time as each segment is processed, coloring all 20,484 vertices with predicted BOLD signal.
- Generates a Cortical Brand Intelligence Report across 10+ sections:
- 7 composite scores (Brand Impact, Recall, Attention, Multimodal Synergy, Emotional Signal, Memory Encoding, Cortical Depth)
- Per-region breakdowns for Visual (V1/V4/MT+), Auditory (A1/Belt/STS), Language (Broca 44/45, Wernicke, Angular), and Executive (DLPFC/ACC/FEF) cortex
- Cortical processing order (V1 → A1 → MT+ → Broca → Wernicke → DLPFC)
- Multimodal integration score — measures the gain at the temporal-parietal-occipital junction (TPOJ) when all three modalities converge
- Memory encoding quality (sustained Broca + Wernicke predicts 24h recall)
- Language left-lateralization index
- Emotional signal from Angular gyrus / TPJ
- Prioritized risk flags and optimization recommendations
- Timeline scrubber — a segment-by-segment cortical timeline below the brain. Click any segment to rewind the mesh to that exact moment, showing which regions were active at that second.
- 200 AI consumer personas react in real-time with comments as the analysis streams in — across generations, industries, psychographics, and global markets.
- Ideation workspace — describe your product, get AI-generated target segments, hooks, KPIs, and platform fit visualized as an interactive React Flow graph with web-sourced evidence via Scout.
- Voice agent — conversational interface for querying your brand report hands-free using Vapi.
Every metric has a plain-English label so non-neuroscientists can act on data immediately.
| Layer | Technology |
|---|---|
| API server | FastAPI + uvicorn |
| Real-time streaming | WebSockets (asyncio queue per job) |
| Brain model | TRIBE v2 (Meta FAIR) — tri-modal fMRI foundation model, 720 subjects |
| Cortical atlas | Destrieux surface atlas on fsaverage5 via nilearn — 17 anatomically correct ROIs |
| Audio transcription | whisperX |
| Tensor framework | PyTorch |
| Numerical computing | NumPy |
Key backend files:
backend/inference.py— TRIBE v2 wrapper + Destrieux atlas loader. Maps 20,484 fsaverage5 vertices to 17 ROIs across 4 cognitive dimensions.backend/scoring.py— Deterministic brand scoring engine: 15+ metrics computed from cortical activations (multimodal synergy, emotional signal, memory encoding, language lateralization, peak latencies, visual hierarchy check, risk flags).backend/main.py— FastAPI app:/api/analyzeupload endpoint +/ws/brain/{job_id}WebSocket stream.
| Layer | Technology |
|---|---|
| Framework | Next.js 15 (App Router) |
| Language | TypeScript |
| 3D brain rendering | Three.js — OBJ mesh loader, vertex colors, OrbitControls, raycasting for click-to-inspect |
| Ideation graph | React Flow — auto-layouted node graph of segments, hooks, KPIs |
| Animations | Framer Motion |
| Auth | Auth0 + Supabase |
| State | Zustand |
| Database | MongoDB + Mongoose |
| AI | Cohere (embeddings + generation) |
| Voice | Vapi |
| Web research | Playwright + Cheerio (Scout crawler) |
The 3D brain mesh is an fsaverage5 cortical surface (10,242 vertices per hemisphere, 20,484 bilateral). ROI assignments come from the Destrieux surface parcellation via nilearn. 17 ROIs mapped to 4 cognitive dimensions:
| Dimension | Regions |
|---|---|
| Visual | V1, V2/V3, V4/FFC, MT+/MST, V-dorsal |
| Auditory | A1 (Heschl's), A-belt, STS |
| Language | Broca 44, Broca 45, Wernicke, Angular, STS-lang |
| Executive | DLPFC, FEF/SMA, ACC, Superior Frontal |
All metrics are computed deterministically from TRIBE v2 BOLD predictions — no LLM, no lookup table.
| Metric | Formula |
|---|---|
| Brand Impact | 0.40 × attention + 0.35 × recall + 0.15 × (1 − exec_penalty) + 0.10 × sensory_mean |
| Recall | 0.55 × language_processing + 0.45 × memory_encoding |
| Attention Retention | 0.40 × visual + 0.30 × auditory + 0.30 × language − executive_penalty |
| Multimodal Synergy | Geometric mean of V/A/L modalities × balance factor (TPOJ convergence gain) |
| Memory Encoding | Sustained Broca + Wernicke BOLD (phonological loop + semantic encoding proxy) |
| Emotional Signal | Angular gyrus + TPJ sustained activation |
| Language Lateralization | Broca 44/45 left-hemisphere dominance index |
Key neuroscience findings incorporated into the scoring model:
- FFA activates for faces, PPA for scenes, EBA for bodies, VWFA for written text
- Language response propagates from primary auditory cortex (~3s) to inferior frontal gyrus
- Left-hemisphere lateralization is a reliable predictor of language encoding
- Maximum multimodal gain occurs at the TPOJ when audio, visual, and language signals are temporally aligned
- Five ICA components identified: auditory, language, motion, default mode, visual
TRIBE v2 paper: d'Ascoli et al. 2026 (Meta FAIR)
cd backend
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pip install whisperx nilearn torch
uvicorn main:app --port 8001 --reloadOn first run, TRIBE v2 weights (~4 GB) are downloaded from HuggingFace (facebook/tribev2) and cached locally.
cd frontend
npm install
npm run devApp runs on http://localhost:3000.
| Variable | Description |
|---|---|
NEXT_PUBLIC_BACKEND_URL |
FastAPI server URL (default http://localhost:8001) |
AUTH0_SECRET / AUTH0_CLIENT_ID / AUTH0_CLIENT_SECRET |
Auth0 credentials |
NEXT_PUBLIC_SUPABASE_URL / NEXT_PUBLIC_SUPABASE_ANON_KEY |
Supabase project |
MONGODB_URI |
MongoDB connection string |
COHERE_API_KEY |
Cohere API key |
VAPI_API_KEY |
Vapi voice agent key |
nemo/
├── backend/
│ ├── main.py # FastAPI server, WebSocket streaming
│ ├── inference.py # TRIBE v2 wrapper, Destrieux atlas ROI loader
│ ├── scoring.py # Brand scoring engine (15+ cortical metrics)
│ └── requirements.txt
└── frontend/
└── src/
├── app/
│ ├── analyze/ # Core analysis workspace — upload, 3D brain, report
│ ├── tunnel/ # Ideation workspace — React Flow segment graph
│ ├── dashboard/ # Project & session management
│ ├── projects/ # Project list view
│ ├── report/[id]/ # Shareable report page
│ ├── compare/ # Side-by-side brand report comparison
│ └── voice-agent/ # Vapi conversational interface
└── components/
├── brain-3d.tsx # Three.js cortical surface renderer
├── simulation-dashboard.tsx # 200-persona live feed
├── voice-agent.tsx # Vapi voice interface
├── flow/ # React Flow nodes + drawers
└── globe-3d.tsx # 3D globe visualization
v1.1 — Shareable report URLs, PDF export, segment comparison overlays
v1.2 — Multi-video A/B testing, frame-level attention heatmap export, Slack integration
v2.0 — Swarm intelligence layer — scale from 200 personas to millions of simulated consumers using distributed agent swarms, enabling statistically significant population-level cortical predictions across global demographics in real time
Built by Ishaan Goel