Core Technology

Visual intelligence,
precisely delivered.

Our proprietary computer vision engine processes millions of pixels per second to generate a structured, hierarchical taxonomy for every asset in your library.

Try 100 images free Read the API docs
Nexa interface showing real-time AI tagging overlay on a product image
Processing: 0.3s · Confidence: 98.4%

The Pipeline

From pixels to structured data

Nexa doesn't just guess; it analyzes. Our multi-stage pipeline breaks down every image into semantic components, ensuring that every tag is contextually accurate and hierarchically sound.

1. Ingestion & Pre-processing

Raw files are normalized, resized, and color-converted to a standard sRGB space. We strip EXIF metadata to protect privacy while preserving visual data. This ensures consistent input for every model in the pipeline.

Supported formats: JPEG, PNG, WebP, TIFF, RAW (CR2, NEF, ARW), HEIC, SVG.

2. Multi-Modal Analysis

Nexa runs parallel neural networks: a CNN for object detection, a Vision Transformer for semantic understanding, and a color science model for palette extraction. These outputs are fused into a single context vector.

Output: A JSON object containing bounding boxes, tags, colors, and sentiment scores.

Tag Taxonomy

Deep semantic understanding

Beyond simple object recognition, Nexa understands context, emotion, and style.

Objects & Products

Granular identification of specific items, materials, and textures. Recognizes brands, models, and proprietary products with high precision.

People & Identity

Demographic analysis including age range, gender, and expression. Privacy-safe by default; no biometric data is stored or used for training.

Emotions & Mood

Sentiment analysis of facial expressions and overall scene atmosphere. Tags like "joyful", "melancholic", "energetic", or "serene" are generated automatically.

Colors & Palettes

Extracts dominant and accent colors using Lab color space analysis. Generates CSS-compatible hex codes and named color tags.

Locations & Context

Geospatial tagging for street scenes and indoor environments. Recognizes architectural styles, weather conditions, and time of day.

Abstract Concepts

Style classification including photography genre, art movement, and composition techniques (e.g., "Golden Ratio", "Rule of Thirds", "Bokeh").

Precision Control

Fine-tune the engine to your needs

Control the balance between recall and precision with confidence thresholds, and train the model on your specific brand assets.

Confidence Thresholds

Every tag comes with a confidence score (0.0 to 1.0). Use the threshold slider to filter out low-confidence noise. Set a high threshold for strict cataloging, or a low one to capture every potential match.

Min Confidence 85%

Custom Vocabulary Training

Upload a CSV or JSON file of your specific product SKUs, brand names, or campaign terms. Nexa will retrain the model on your dataset within minutes, ensuring your unique terminology is recognized instantly.

Enterprise only. Requires a minimum of 50 labeled examples per custom class.

Performance

Benchmark comparison

Nexa outperforms manual tagging and general-purpose competitors in both speed and accuracy.

Metric Manual Nexa AI Competitor X
Tagging Accuracy ~65% 98.2% 91.5%
Processing Speed ~4 hrs / 1k 0.3s / img 1.2s / img
Contextual Understanding Low High Medium
Custom Vocabulary Manual Entry Auto-Training Manual Entry

Developers

Integrate in minutes

A RESTful API designed for speed and reliability. Get structured JSON responses directly from your code.

bash POST /v1/tag 
{
  "image_url": "https://cdn.nexa.ai/assets/2023/summer-campaign.jpg",
  "threshold": 0.85,
  "return_bounding_boxes": true
}

Response (200 OK)

{
  "id": "tag_8f92a",
  "status": "completed",
  "processing_time_ms": 312,
  "tags": [
    { "name": "product", "confidence": 0.98, "category": "object" },
    { "name": "summer", "confidence": 0.94, "category": "season" },
    { "name": "golden_hour", "confidence": 0.91, "category": "lighting" }
  ]
}
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