Images

Every asset sharp enough for where it ships.

This is an example of an agent you can build with Sylvie: point it at a logo, a product shot or an old campaign photo and it rebuilds the file at the exact resolution its destination needs, from a marketplace zoom image to a billboard, without the detail going soft. It runs on your brand brain, so it upscales to the delivery specs each channel and client actually require, keeps product detail faithful to the real thing, and only touches the master assets this brand is cleared to use. Sylvie does not ship it pre-built; your team assembles it against everything the brain already knows about how this brand delivers images.

Example agentImages

Sylvie does not ship this agent pre-built. It is an example of an agent your team can build with Sylvie, powered by your brand brain, so it runs your workflow the way you actually run it.

What it is

What is the Image Upscaler agent?

The Image Upscaler agent is an example of what a marketing team can build on Sylvie: a production step that takes an undersized or soft image and rebuilds it at the resolution, format and quality bar the job calls for. It handles the enlargement, the sharpening and the export, then routes each file to the spec its destination demands, so a 400-pixel logo becomes billboard-ready and a web-only product shot clears a marketplace's zoom requirement. It is not a one-size slider you re-set every time; it is an agent configured once against the brain that knows how this brand delivers images.

For agencies and in-house teams, the low-res file is a deadline problem disguised as a technical one. The client sends the only logo they have at thumbnail size, the campaign shoot from three years ago only lives at web resolution, and the out-of-home deadline does not move. An agent built on your brand brain fixes the file to the exact output spec without a designer stopping their day, so the asset ships on time and still looks like the brand meant it.

How it works

How the Image Upscaler agent works

Before it touches a file, the agent reads the brand brain: the delivery specs each channel and client work to, the resolution, format and aspect ratio a destination requires, the sharpening and quality standard this brand has agreed, the product-accuracy rules it must respect, and which master assets are cleared for use. Because the brain is permission-aware, it only draws on the files this brand is licensed to touch, and nothing crosses over from another client's account.

Then it runs the job end to end inside Sylvie: it reads where each asset is headed, rebuilds the image to the matching resolution, holds the real detail while it sharpens, exports in the right format and hands back files ready to ship, one at a time or a whole batch at once. You review the outputs in the same place, and every setting you lock and every correction you keep teaches the brain, so the next run comes out closer to how your team likes to finish an image.

Key benefits

Why teams build this agent

01

Built for the destination

Every file comes out at the resolution, aspect ratio and format its channel needs, so nothing gets rejected by a marketplace or bounced back from a printer.

02

Faithful to the product

The agent enlarges the detail that is there without inventing new detail, so a product's real shape, finish and label text stay accurate instead of being guessed by a model.

03

Whole batches in one pass

A full catalog or a shoot's worth of selects clears to spec together, so the work that used to eat an afternoon in an editor happens while you do something else.

04

Your quality bar, applied

Sharpening and output settings follow the standard this brand has already agreed, so upscaled assets match the ones your team would have hand-finished.

05

No new licensing risk

It only works on the master assets this brand is cleared to use, so nothing pulled from another account or an unlicensed source slips into a deliverable.

06

Designers freed for design

Routine enlargement stops landing on senior people, so your team spends its hours on craft that matters instead of dragging sliders in an editor.

Use cases

Where it goes to work

A few of the resolution jobs a team builds this agent to handle, each file rebuilt to the spec the brand brain already holds. The outcome is the same every time: the asset ships at the size its channel needs and still reads as the brand.

The client logo that only exists tiny

The only file the client can find is a 300-pixel PNG, and it needs to go on a trade-show banner by Thursday. The agent rebuilds it to full print resolution against the brand's own logo specs, so the banner goes to the printer clean instead of the account team chasing a vector file that may not exist.

The archive shoot going to print

A campaign photo from three years ago only lives at web size, and it is the hero of the new brochure. The agent enlarges it to press-ready resolution while holding the detail, so a reshoot worth thousands is off the table and the brochure keeps the image everyone already approved.

The marketplace that rejects your images

Product shots keep bouncing because they miss the marketplace's zoom minimum. The agent takes the batch to the required pixel size in one run, so the listings go live the same day instead of stalling in a back-and-forth over image requirements.

The soft hero on a 4K display

Your landing page looks crisp on a laptop and mushy on a retina screen. The agent delivers the hero and background assets at the density modern displays need, so the site reads as premium on the devices your best prospects actually use.

The influencer frame you want to reuse

A creator sent great content, but a still you want for paid social came in low-res. The agent upscales the frame to ad spec while keeping it true to what was shot, so the asset clears the ad platform's quality check instead of getting flagged.

The full catalog rebuilt to one spec

A new retailer needs every product image at a consistent, larger size. The agent processes the whole library to the single spec in a pass, so a week of manual exporting becomes an afternoon and the catalog ships uniform.

FAQ

Questions, answered

A generic tool upscales to whatever setting you happen to pick and forgets the job the moment it finishes. This is an agent you build on your brand brain, so it already knows the output spec each channel and client needs, the quality bar this brand holds and which assets it is allowed to touch. You stop re-entering the same settings on every file; the knowledge lives in the brain and the agent applies it.

You build it. Sylvie does not ship the Image Upscaler agent pre-packaged; this page is an example of what a team assembles on the platform. Sylvie helps you wire it to your brand brain and to the delivery specs you actually ship against, so the production step you end up with is yours.

That is the reason to build it on the brand brain rather than a raw model. The agent enlarges the detail that is there instead of inventing new detail, and it follows the product-accuracy rules this brand has set, so labels, finishes and shapes stay true. You review outputs in the same place, and anything you correct teaches the brain for next time.

The delivery specs live in your brand brain: the print DPI, the out-of-home dimensions, the marketplace pixel minimums, the social and web sizes each client works to. The agent reads where a file is headed and rebuilds to the matching spec, so you are not looking up requirements by hand. Because the brain is permission-aware, it only draws on the assets this brand is cleared to use.

Always. You decide the quality bar, the formats, the sharpening and the rules the agent must follow, and you can override any output. Every setting you lock in and every correction you keep teaches the brain, so the agent's default gets closer to how your team likes to finish an image.

Each brand brain is isolated and permission-aware, so one client's assets and specs never surface in another's work, and people only reach what they are cleared to use. The agent runs on top of your brain inside Sylvie; your images and your standards stay yours and are not pooled to train a shared model.

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