Visual Intelligence

Visual Intelligence Extraction

Overview

Extract business intelligence from video frames — facilities, products, team, branding, equipment — using frame extraction + AI multimodal vision. A 2-minute YouTube vlog can yield 7+ intelligence findings that NO text source (transcripts, websites, aggregators, filings) could reveal. Visual analysis is not optional — it's a distinct intelligence layer.

When to Use

• Target company has YouTube videos showing offices, warehouses, products
• "Day in the Life" or vlog content exists
• Event/conference videos with sponsor banners
• Product demo videos with visible brand names/models
• Any video where WHAT YOU SEE matters more than what's said

Prerequisites

• yt-dlp — video download (pip install yt-dlp)
• ffmpeg — frame extraction (brew install ffmpeg)
• Multimodal AI model — Claude (Read tool on JPG) or GPT-4V for frame analysis
• Pillow — optional, for building contact sheets when frame count exceeds 20 (pip install Pillow)

Method

Step 1: Download Video

Download at 720p — sufficient resolution for visual analysis, saves bandwidth and disk.

yt-dlp -f "bestvideo[height<=720]+bestaudio/best[height<=720]" \
  --merge-output-format mp4 -o "VIDEO_ID.mp4" VIDEO_URL

For metadata-only checks (duration, title, upload date) before committing to download:

yt-dlp --skip-download --print "%(title)s | %(duration)s | %(upload_date)s" VIDEO_URL

Step 2: Extract Frames

Choose extraction rate based on video length:

# Short video (<2 min): 1 frame every 5 seconds
ffmpeg -i video.mp4 -vf "fps=1/5" frames/frame_%03d.jpg

# Medium video (2-10 min): 1 frame every 10 seconds
ffmpeg -i video.mp4 -vf "fps=1/10" frames/frame_%03d.jpg

# Long video (10+ min): 1 frame every 30 seconds
ffmpeg -i video.mp4 -vf "fps=1/30" frames/frame_%03d.jpg

# Keyframes only (scene changes — most efficient for edited content)
ffmpeg -i video.mp4 -vf "select='eq(pict_type,I)'" -vsync vfr frames/keyframe_%03d.jpg

Step 3: Build Contact Sheet (if >20 frames)

Grid frames into a 6x6 contact sheet for batch review. This reduces tool calls by 30x.

from PIL import Image
import glob

frames = sorted(glob.glob('frames/*.jpg'))
cols, rows = 6, 6
thumb_size = (200, 150)

sheet = Image.new('RGB', (cols * thumb_size[0], rows * thumb_size[1]), 'white')
for i, frame_path in enumerate(frames[:36]):
    img = Image.open(frame_path).resize(thumb_size, Image.LANCZOS)
    x = (i % cols) * thumb_size[0]
    y = (i // cols) * thumb_size[1]
    sheet.paste(img, (x, y))

sheet.save('contact_sheet.jpg', quality=85)

If more than 36 frames, generate multiple sheets and analyze sequentially.

Step 4: AI Vision Analysis

Analyze each frame (or contact sheet) systematically. For every frame where content is identifiable, report:

1. FACILITY — Office or warehouse? Commercial or residential? Size estimate?
2. PRODUCTS — Equipment visible? Brand names? Model numbers? Condition?
3. PEOPLE — How many? Roles (from clothing/context)? Gender? Family?
4. BRANDING — Company logos? Partner logos? Event banners? Merchandise?
5. SIGNAGE — Readable text? Addresses? Phone numbers? Certifications on walls?
6. EQUIPMENT — Vehicles? Forklifts? Computers? Software on screen?
7. SCALE — Inventory levels? Number of bays? Parking lot size? Attendee count?

Step 5: Cross-Reference Against Dossier

Compare visual findings against existing text-based intelligence:

• "Commercial office" claim → does the video show residential walls and carpet?
• "10 employees" → how many people visible?
• "Fleet of vehicles" → personal car with no branding?
• "Enterprise customers" → customer site shows 8 cabinets, not hyperscale?

Visual evidence CONFIRMS or CONTRADICTS text claims. Flag discrepancies.

Checklist by Video Type

Vlogs / Day in the Life

• Office type (home vs. commercial vs. coworking)
• Vehicle (company truck? Personal car? Branded?)
• Team members visible (count, roles)
• Computer/software visible on screen
• Family members (founder concentration signal)
• Neighborhood/location clues

Product Demos

• Brand names on equipment (manufacturer ID)
• Model numbers visible (SKU validation)
• Packaging (new vs. refurbished indicators)
• Condition (scratches, dents, wear)
• Shipping/logistics setup

Events / Conferences

• Booth size and positioning (budget signal)
• Sponsor banner logos (partnership evidence)
• Attendee count estimate
• Speaking slot (stage, panel, or just booth?)
• Other company reps visible

Facility Tours

• Square footage estimate (ceiling tiles, people for scale)
• Loading dock (truck access = real warehouse)
• Racking systems (inventory capacity)
• Office area (desks = headcount)
• Vehicles in parking lot (employee count)
• Signage (address, hours, certifications)

Anti-Patterns

• Don't skip short videos — even 30-second Shorts show products/facilities
• Don't ignore backgrounds — certificates on walls, whiteboards, signage all carry intel
• Don't assume professional = large — home office does not mean small revenue
• Don't download full video if you only need frames — use --skip-download for metadata, download only when visual analysis is needed
• Don't analyze blurry frames — flag as "ZOOM NEEDED" and re-extract at higher resolution or specific timestamp

Real-World Results

BROGAV Solutions case study (June 2026):

• 1 video (2 minutes, "CEO Data Center Day in the Life")
• 25 frames extracted at fps=1/5
• 7 NEW findings not discoverable from any text source:

Total time: 5 min download + 1 min frame extraction + 10 min visual review = 16 minutes for 7 findings.

Key insight: Visual analysis is a distinct intelligence layer. Text sources (transcripts, websites, aggregators, filings) cannot reveal what only the camera captures.