Name and keywords for images-AI-powered tool to optimize image metadata for search and discoverability.

Example

Generated title example for a rainy night city scene: Neon-soaked Shibuya Crossing at night in Tokyo, long-exposure street scene with umbrellas, reflections on wet pavement, bustling cityscape in Japan, vibrant travel photography nightlife

Scenario

A stock contributor uploads 60 urban night photos from Tokyo. For each image, I produce a single 150–190 character title that front-loads the main entity (Shibuya/Tokyo), states the scene and technique (long exposure, street), and adds distinctive attributes (rain, umbrellas, reflections). This yields consistent, richly descriptive titles that perform better in marketplace search and remain within platform length guidelines.

Example

50 single-word keywords for the same scene: Shibuya, Tokyo, Japan, night, nightlife, neon, umbrellas, rain, rainy, pavement, reflections, cityscape, urban, street, streetscape, traffic, crowd, pedestrians, crosswalk, intersection, longexposure, bokeh, lights, glow, wet, weather, travel, tourism, asia, metropolis, vibrant, colorful, saturated, motion, speed, blur, dusk, evening, nightscape, modern, architecture, skyscrapers, advertising, billboards, retail, shopping, culture, lifestyle, energy, travelphotography

Scenario

A travel photographer wants 50 consistent single-word keywords per image across a 200-photo set. I deliver a balanced mix: entities (place names), concrete attributes (weather, technique), and intent-bearing concepts (tourism, nightlife). I avoid multi-word phrases, remove duplicates, and prefer specific over generic (cityscape over city). This improves matching against user queries without violating marketplace rules.

Example

Before (user keywords): tokyo, Tokyo, shibuya crossing, city, asia, neon lights, rain, rainy, night, people, city, fun, travel, advertising, shop signs After (cleaned): Tokyo, Shibuya, crosswalk, cityscape, Japan, neon, rain, rainy, night, pedestrians, travel, advertising, billboards Fixes applied: deduped capitalization (tokyo/Tokyo), converted multi-word phrases to single tokens (shibuya crossing → crosswalk; neon lights → neon; shop signs → billboards), replaced vague city with cityscape, increased specificity (Asia → Japan), standardized style (singular/plural forms).

Scenario

A studio prepares a batch of 300 images from multiple shoots. I provide a reusable template with token slots (e.g., {city}, {season}, {technique}) to generate consistent titles and keyword sets per image, then run QA to enforce single-word terms, remove stopwords and duplicates, correct spelling, and rank high-intent terms toward the top. This reduces manual labor, improves metadata quality, and keeps entire collections consistent.