DAM, Daryl. He got it.
He lives inside a smart DAM built for marketing teams: a complete asset library when you need one, or the intelligence that works across a supported library you already use.
You may remember the exact shot but not its filename. Or you may need an image for a new campaign and want to see what is already in the library.
Daryl is built around finding those existing assets. The request and supplied brand context help explain relevance; you inspect the evidence and choose.
That's the problem Daryl exists to solve.
Explore two authored requests: an image description and a social post idea. Each shows existing sample assets, visible reasons and details to check. Brand context adds relevance while product, color and exclusions remain explicit. This is an illustration, not a live result.
Illustrative example · authored requests and results, not a live search. AI-generated sample assets.
Keep in view: ivory bottle · outdoors · black cap · no studio shots
A seated person and foreground essentials suggest a break outdoors; the earthy colors fit the supplied brand direction.
Brand + work: A seated person and foreground essentials suggest a break outdoors; the earthy colors fit the supplied brand direction.
Check: AI-edited framing; not a verified product photograph. Check product details before use.
The person, winding trail and natural light support an everyday outdoor story.
Brand + work: The person, winding trail and natural light support an everyday outdoor story.
Check: The bottle is less prominent. Inspect product detail and the final crop.
A close view of the bottle beside a stream gives the outdoor pause a product focus.
Brand + work: A close view of the bottle beside a stream gives the outdoor pause a product focus.
Check: Check the actual product and version before use. The hand and bottle dominate the frame.
Brand context adds relevance. Fieldwork’s supplied direction: natural light, earthy colors, everyday outdoors. It helps assess fit; it does not override the requested product, color or exclusions.
Example requests are fixed. In the product, image descriptions use Library search; social post ideas use Post Intelligence. These are separate workflows.
We are building DAMdaryl around a simple job: help marketing teams find existing assets for what they are making.
A modern brand team asks both “where is the file?” and “which approved asset fits this moment?” A smart DAM should answer both without hiding the library behind a chatbot.
So Daryl was built into the library itself. Brand Context, semantic discovery, visible Brand Analysis, and work-based recommendations turn the collection into a system that can help a marketer choose.
Daryl is the character behind the smart DAM. His job is to help you compare the assets you already have against your brand and what you’re making, with reasons you can inspect.
He doesn't replace the call you make. He makes it easier to inspect: what the work needs, which assets plausibly fit, and why one leads the rest.
DAM, Daryl. He got it.
Matt Cronin and Jordan Bratter, co-founders
We’re building DAMdaryl to help marketing teams find existing assets for the work at hand, with reasons grounded in their brand.
Ask us about Daryl →Four principles behind the way the smart DAM behaves.
Describe a remembered shot, the image you need, or the marketing work. Include literal requirements and exclusions; a formal brief is optional.
Brand Context gives the choice a foundation. The current direction supplies the job: an asset can fit the palette yet miss the requested human moment.
Look for the visible detail, its connection to the brand and the work, and the tradeoff. Missing evidence should stay visible rather than become a confident claim.
Inspect the candidates and choose your next step. A recommendation supports the decision; it does not replace the team’s creative judgment.
Build a complete library or connect a supported one. Then tell Daryl what you’re working on.