Natural Language Search (NLS) lets you find assets by describing them in everyday language, instead of relying on tags, metadata, or smart filters. You can locate an image even when it has no metadata at all, and search in more than 100 languages regardless of how your taxonomy is set up. With NLS, you describe in your own words what you are looking for, and Bynder’s AI search finds the assets that match what you meant.
Search without the guesswork
Metadata is a time-consuming manual work that’s never perfect. Tags go missing or get applied inconsistently across teams and campaigns, or exist only in the language spoken by the market in which the asset was uploaded. NLS reads for meaning or intent rather than relying on an exact tag match, so a description close to what’s actually in an asset is often enough to find it, even when the metadata behind it is incomplete, missing, or written in a different language.
NLS is just as useful to someone who has never learned how your library is tagged as it is to a longtime DAM admin: you don’t have to tag everything perfectly, or in every language, for your team to be able to find the asset they need.
Where this shows up most:
- You know what an asset is for, but not what it’s called
- You’re describing something visual (a mood, a setting, a composition) that was never tagged as a single term
- Your team uses specific industry vocabulary that a generic keyword search was never built to understand
- Your team searches in a different language than the one your assets were tagged in
- You’re a DAM admin trying to track down assets that were never tagged, so you can enrich them
NLS works alongside your existing tags, not instead of them.
From description to discovery
The best natural language queries follow an “object + attribute + context” formula. Describe the subject, the setting or mood, and any specific requirements (orientation, background, color) in one sentence.
For example, instead of trying several combinations of tags, try:
“A product shot on a white background, no shadow, vertical, for a product page”
“The campaign photo with a model laughing near a window, natural light”
You don’t need to already know the subject either. At a global agriculture customer, a team lead new to the company’s science vocabulary got a request for a “wheat field in early stages, with focus on leaves showing powdery mildew”. She typed exactly that “wheat field, early stages, powdery mildew” into the NLS and found it without first needing to learn the technical term for what she was looking for.
If the first result isn’t quite right, you don’t have to start over, just refine your description. The more specific you are, the more accurate NLS gets.
Natural Language Search, in our customers’ words
Productivity increased through natural language and similarity search.
Manual duplicate removal was eliminated, resulting in significant time savings.
Accelerating video localization from 2‑5 weeks down to just 2‑5 hours.
Syngenta, a global agri-tech company with more than 33,000 employees, manages over 1,000 products across 90+ countries and four DAM instances, one of them holding more than 110,000 assets. At that scale, with seasonal product variations and highly specific agricultural terminology, keyword search alone couldn’t keep up. Finding the right asset could take weeks.
AI Search transformed how we work. It turned finding assets from weeks into minutes and made our teams faster and smarter.Ilektra Apostolopoulou
Global Marketing Product Manager at Syngenta
As part of a broader AI Search rollout that included Natural Language Search, Syngenta’s team could search using the same agriculture-specific terms they already used day to day, instead of needing to know how each asset happened to be tagged.
