Yess.. Another great technology partner for EnhanceThat.
Miro and EnhanceThat are partnering up to develop fashion workflows.
And I’m so excited about this one. Great news for teams already using Miro.

But first, this confirms what we believed from the start. Fashion doesn’t need more tools. Existing tools need to be connected. That is what makes efficient workflows possible, in place of siloed tools.

And our growing group of partners helps us build such workflows. Knowing the people behind the tools, knowing the APIs, being able to work together. It just makes building something easier. And damn, it is inspiring to see what is happening in the kitchen!

Nobody forced designers and merch to use Miro

Miro holds a special place for me. In the 20 years I’ve been pushing digital ways of working in sportswear and fashion, Miro came as a relief.

It is by far the fastest and most affordable bottom-up adoption of a tool I’ve seen in fashion.

Shows up with design teams making their collection boards.
At sketch reviews
Visual line planning
Colour reviews
Store planning
Line reviews
Even at go-to-market meetings, collecting sales feedback.

And all of that without fancy integrations or fashion-specific features.
No big transformation program or army of consultants.
People just started using it. That is what I love about Miro.

What our workflow review data is showing

In our workflow reviews, we measure manual operations and friction across fashion workflows. And we started seeing something interesting.

Making a line plan or collection board itself isn’t necessarily the problem.
Keeping it up to date is.
Teams make the most amazing line plans, style sheets, and color overviews in Miro. But they still need to look up style names and prices, pull in images from different tools and manage all the updates happening day by day.

And since so many of our clients already use Miro, we started thinking: why not bring the data to the place people love to work, instead of the other way around?

It has an immediate impact:

Less context switching. No jumping between PLM, Miro and design tools to copy-paste data and images. Stay where you are. Keep your focus.

Boards that stay up to date. Line plans, style sheets, material boards: you know you’re looking at the latest product data and images. And not everyone needs to work in PLM anymore.

People can work in the tools they actually like to use. This is an important one for anyone responsible for digital transformation. Instead of forcing everyone into PLM, bring PLM to where people already work.

Much less change management. If Miro is already adopted, don’t introduce another big system. Put the transformation effort into improving the process or introducing the specialist tools that actually add something, and connect them into the workflow.

That last one is probably more important than it sounds.
Investing in new technology doesn’t only cost money. It takes time, attention and creates noise. Sometimes the smarter move is to make the technology people already use work better together.

And I’m not going to shower you with endless integration details now :)
If you want to know more, you know where to find us.

dom@enhancethat.fashion

More freedom to create, that is the purpose.

This summer we’re working with Stieglitz to bring Design Sell Make into practice.
We’re doing a couple of things.

• We’re bringing new designs to market as preorders.

• We’re cutting design to market down to 6 weeks.

• And maybe most importantly, we’re linking design to production and cutting the inefficiencies in between.

Linking design to production

Important, because if we want to cut time to market, this is where there is a lot to win.
And if we want to start selling before we make, we have to make sure the design is locked in with manufacturing. Otherwise we’re selling something that can’t be manufactured. That’s a problem.

That is why we were in Istanbul this summer, visiting Dayteks, the supplier we work with for this project.

Linking design to production has to do three things:

First: can the factory read the tech pack without interpretation issues? Which kind of means: how do we make sure the factory doesn’t have to fill In design gaps?

The role of the factory should be to say: yes, I can manufacture this. Or no, I can’t, and here’s what I suggest. It is the technical validation before the design renders go online. Once we start selling, the design can’t suddenly change.

Second: does the factory have enough information to give us a commercial price?

Third: can we do both of those things without kicking off a prototype waterfall? We want to keep investment low and the calendar short. Every proto adds time and money.

Uhh is that a Tech Pack, a AI techpack?

It may look like one. But don’t judge it purely on what you see.

The difference between a random tech pack and one that really links design to production is not how it looks. It’s what sits behind it: the process, data and habits around it.

Where it often goes wrong:

• Leaving design decisions open, thinking: we’ll see how the factory solves this during prototyping.

• Tech packs created manually, so they differ from person to person.

• Not referring to standardised fits, materials or constructions.

Making existing tech packs a little better or easier to create is not enough if we want it to change the calendar. We have to turn design data into manufacturing data with no room for interpretation and the least amount of manual entry.

The approach

First, we looked at what data is really needed.
Too much information can be contradictory. And every data point that is in there needs to be kept up to date. More data isn’t automatically better data.

Second, we standardised the main inputs. Fabrics, fits, trims, labels and placements. Standardising what repeats takes out noise. It makes automation possible and removes another source of interpretation.

And yes, third: automation. Connected data. The tech packs we started making are now around 80% automatically generated. When the structure and system are consistent, it becomes much easier to read, spot the differences and understand what actually changed.

And that data can connect to the factory’s ERP, taking out another interpretation risk and another manual entry point.

With the technology we have available now, we can radically change the way we do things. But only after we change some of those habits and processes.

Reminder: why are we doing this again?

Close that communication gap between design and production and you open up massive creative and commercial opportunities.

Get closer to the market → test demand → take more creative risks → make bigger decisions with better information.

This design to production work is one part of the Design Sell Make workflow we’re developing. Together with Stieglitz, we’re now piloting that workflow on a real drop.

This week, the first drop from the pilot goes live.
Ready to capture all the learnings and tell you how it worked out.
Keep you posted.

--
I love this kind of work.
I started my career as a garment developer, a technician, visiting factories and solving these exact problems.

Back then, I learned how to rethink the process. Standardise what could be standardised, remove unnecessary steps and leave more time for the things that actually mattered.

Now I’m back at it, same problem, much bigger opportuities.

This is how it works

• Analyze your fabric library, take in your studio setup and style guide.

• Extend the workflow with an AI pass that applies your Brand DNA to the image

• And the automation will deliver the right size and quality image to your downstream systems.

The result: packshot-quality images for your review, no manual work, no prompting battle.

It is a repeatable process, which means you can scale it and expand it. Build on top of it, zoom-in shots, on-body, full outfits, you name it. And here, our core principle applies: it is tool-agnostic. Tech is constantly changing, so there are better AI tools, render engines, or settings. Everything can be updated without breaking the user’s experience.

Flexibility matters.

This is repeatable, it scales, and when a better AI tool or render engine comes along, we swap it. We believe this is the way to keep workflows at the top of what is possible without a long, expensive rollout and retraining.

If this resonates, you know where to find us.

And every year we came to the same conclusion. Designs look more realistic, adoption gets better but bottom line the business impact is disappointing. We don’t see shorter calendars, less inventory or significantly better margins.

This simple rule applies every time: New tools x Old process = same old outcomes. Tech alone is not going to change fashion.

That’s when I came across the work of Polish artist Edward Krasiński, who spent decades connecting shapes, spaces and objects with a single roll of blue tape. Six centimetres wide, always at the same height. By connecting things that didn’t belong together, he created something entirely new. It wasn’t about the tape. It was about the connection. That felt right. I called the project Edward.

I decided to stop chasing better tools. Instead I started asking a different question. Not how do we digitise what we have been doing. But with all of the tech and skills we have today, what if we could redesign the way fashion is made. From scratch. What would it look like? What if we stop being obsessed with new tech and shift the focus to how we could reshape the process.

Not slow and gentle, but redesign from the ground up for a new generation of fashion.

I interviewed a new generation of professionals in our industry. Designers, manufacturers, merchandisers. People with a career in fashion and apparel. The outcome of that research was not that they needed more tech. They betted on systemic change. They saw the biggest hurdles being our legacy processes and habits as the main slowdown of change.

Instead of designing what we think we can sell twelve months out, estimating volumes, committing to production, then doing whatever it takes to shift inventory, what if we flipped it?

Design - Sell - Make

Use technology to unlock creativity, stay closely connected to the market and manufacturing, and start selling ideas before you commit to the volumes that turn into waste and liability.

It is now 5 years later. Shein showed you can produce closer to market in smaller batches. With the right use of AI we can generate any type of product and make it look commercial before it is even made. The tools to change the sequence exist.

At EnhanceThat we’re convinced this is the next step for fashion. New tech × new process = business model change. We’re building it.

In the coming weeks we’ll share here the steps we’re taking, from design to manufacturing, and how we’re making digital-first selling a reality with Stieglitz.

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No one got into fashion to rename files and update PLM.
We got into it to craft a fit, build a collection, develop the most inspiring brand. Working in fashion means making decisions at a constant pace.

Along the way, tools multiplied: data in PLM, Miro for reviews, AI that turns sketches into real product, 3D that makes sampling better. All of it is genuinely exciting. But when tools aren’t connected, more and more time is spent moving files and data from one system to another.

When you decide to integrate, where do you start? If anything is possible, what makes most sense?

That’s what we built the Workflow Assessment for. Most consultants prove value after implementation. We bring you proof upfront. We have years of experience connecting tools across fashion brands - which means we can model what happens when your tools talk to each other before you commit to building anything.

In just two weeks, we scan your tech landscape and the way you work, and hand you the plan, the impact, and the numbers behind it.

We get you the numbers, you make the call.
We’ve got the expertise to deliver on what we find.

It runs in three phases. We set the goal together, so measurement has a target. We follow your team and measure real workflow, not documented process. We find where the friction lives, where focus breaks, where time disappears every season, silently, because two tools that should talk to each other don't. Then we model what happens when they do.

Questions we hear a lot

Can we build this internally?
You can map a process. What we bring is years of cross-brand implementation. We know what works, what breaks, and what things cost in practice. The scenarios we build are grounded in that. You’re not paying for theory.

We’ve already mapped our process. Do we still need this?
A process map tells you what happens. The assessment tells you what it costs, where the real friction is, and what changes when you fix it. Most brands we work with have documentation. Very few have impact data.

What do I walk away with?
A friction heatmap of your tech stack. ROI modeling for each automation scenario. A prioritized roadmap with implementation timelines, specific enough to take into a budget conversation on day fifteen.

Can we get started right away? How long does it take to deliver?
Book a call and we'll get you booked in. At the end of the assessment, we build a phased plan around your reality. Once you sign it off, we get started. We have frameworks for most integrations. That means weeks, not months.

Less manual work
Workflow that sticks.
& business model that scales.
Every season

Book a free intro call

Making it scale across a team, a company, or a season that’s where the hard work begins.

The question is how do you make it follow brand consistency without burying your team in an endless maze of prompts. How do you turn AI from a tool into a workflow.

And honestly, this is nothing new. We’ve been here before with 3D, and before that with setting up the Adobe suite. We all want to believe we can skip the structure and go wild. And to some extent you can. AI turns every detailed instruction, design direction, or reference image into something beautiful.

The question is how do you make it follow brand consistency without burying your team in an endless maze of prompts. How do you turn AI from a tool into a workflow.

Manual prompting alone breaks

Prompting feels productive because the first results are surprising.

The trap is that it doesn’t scale. As soon as ideas become product it need refinement and control. Doing that with manual prompting can take hours and makes you want to pull your hair out.

Multiply that across a season and you don’t have a workflow and you have a very expensive digital photoshoot with an unpredictable timeline and unpredictable outcomes.

After running multiple rounds of ecom AI delivery for brands, we kept hitting the same wall. Not every brand had good structured input. No fit library, no material library, no label references. And the design input - usually an Illustrator file - made things worse. Those outlines were never set up as AI input. They’re artist impressions with drawn-on shadows and decorative details that confuse the model and distort the whole process.

What we realised is that AI needs structured data to work consistently. We started to call it Brand DNA. A mix of design library, dev library and brand guidelines, structured for AI use.

A Brand DNA layer fixes this by removing the part of the process that currently lives inside someone’s head. The brand becomes structured input, not a prompt users have to remember.

How we set up the Brand DNA

The funny thing, in most cases the Brand DNA already exists. It’s just not structured yet.

Almost every fashion brand is sitting on the main input layer they need. Their ecom images. Thousands of frames, shot to a tight standard, season after season. The lighting, the framing, the labels, the finishing, the fit. The small consistencies that make a product read, instantly, as yours.

Instead of treating it as output, we use it as input to build the Brand DNA structure.

We’re currently building this for one brand, Stieglitz. It’s early work, but what we’re learning is already changing how we think about AI input. We look at their existing imagery, identify the repeatable patterns - the ones a human eye recognises without thinking. The ones a model needs spelled out and wire them in as the input layer for their AI workflow. We’re now turning this process into a repeatable workflow so we can bring it to every brand we work with.

Consistent Brand DNA input gives us the data to generate consistent output. It’s not perfect yet, but with every model improvement, including the latest OpenAI image generation, the dataset becomes more valuable.

The point is not to make AI generate one good image. The point is to make it generate the hundredth one without anyone starting from zero.

That’s the difference between a tool and a workflow. A tool gives you an output. A workflow gives you consistency.

Build the structured input layer first. Then plug in whatever AI tool you like, we have no stake in any of them. We work with what you’ve got.

Recently, for one of our clients, we again deployed our CLO-SET render automation.

On the surface, it’s just controlling the start and the completion of the render process that’s happening inside CLO-SET. However, as a part of the full workflows, it really ties everything together, also by leveraging our external render dashboard.

The automation is simple but efficient. Works with these features:

• Track the user request to render

• Creates the request to render on CLO-SET with the user render preset (quality/resolution / etc.)

• Monitors the rendering process

  • Dashboard represents all triggered renders, their progress and statuses

• Upon completion of the rendering, the automation will download the rendered assets, rename them if required, and upload them to the destination systems (DAM / PLM / etc.).

  • Yet again, the dashboard reports the status of these operations

Each line monitors the “full circle” of the style during rendering:

• Start of the render with the direct link to the style on CLO-SET

• It’s progress

• Completion (or Failure - then will also leave a comment if it’s known why it failed)

• Status of DAM upload (or the system of your choice)

• Status of SharePoint upload

This automation can be easily deployed to any company using CLO-SET. Upload integrations are easily extensible to other systems, and the dashboard can also be adapted to your needs and branding if needed.

Get In Touch

L.L.Bean had invested heavily in Digital Product Creation. The 3D capability was strong. But as adoption scaled, a quieter problem emerged - designers were spending significant time on work that wasn’t design.

Renaming files. Re-exporting assets. Moving content between tools. Small tasks, but constant ones. Consistency suffered, adoptions slowed down.

The Challenge

Scaling 3D meant the manual overhead scaled with it

Scaling 3D across more categories meant the manual overhead scaled with it. File sizes grew. Handoffs multiplied. Most delays weren’t coming from rendering, they were coming from everything around it.

Designers lost momentum switching between tasks. Leadership couldn’t trust that the right assets were in VibeIQ at the right time. The workflow needed to change.

What We Found

The time study surfaced what everyone suspected but hadn’t quantified

EnhanceThat ran a workflow assessment with L.L.Bean's Digital Product Creation and Product teams. Saving, renaming, re-exporting and moving files was consuming far more time than rendering itself.

The cost wasn't just hours.
Every context switch broke creative momentum.
The case for automation was immediate.

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The Approach

One pipeline. Built around how the team actually works.

EnhanceThat built a seamless pipeline connecting CLO, product data and VibeIQ — designed around how L.L.Bean’s designers and product teams actually work.

Manual handoffs were removed. Naming, exporting and syncing happen automatically. The workflow is fully implemented within L.L.Bean’s environment and built to evolve as tools and requirements change. EnhanceThat continues to support it over time.

The Impact

Numbers that changed the conversation

• From 5+ minutes to 16 seconds — 95% faster

• High-quality images in 7 clicks — 84% less clicking

• Designers spend more time designing, less time managing files

• Assets are consistent across the workflow, with a shared visual language

• VibeIQ receives clean, structured data and the latest 3D assets

• Rework drops. Confidence in the pipeline goes up.

Why L.L.Bean Chose EnhanceThat

Everyone spoke the same language from day one

What It Unlocked

• Manual file handling has been removed from the core 3D workflow

• Disconnected tools now run as a single reliable pipeline

• Designers are designing more and managing less

• Digital Product Creation is positioned to scale across every category

A visual line plan. Styles and data connected in one overview everyone can access.
So basic. So essential. And for so many teams, so painful to maintain.

Our workflow assessments consistently show the same thing: the line plan is one of the biggest sources of manual work in a fashion company. Not because it’s complex. Because the tools aren’t connected.

Export images, download, rename, paste repeat. Every season and with every update made.

We built a connector that pulls images and data from PLM, CLO-SET, DAM - whatever source you’re working from. It drops them into the line plan, consistent and with the data you want to see. Miro happens to be where most fashion teams already work, so that’s where we built it. No extra tools to introduce. Just connect what you have.

It’s one of those cases that reminds me why we do this. We’re not building another tool. We’re making the tools you already have work better together.

Simple. And it solves one of the main friction points we see repeatedly in our workflow assessments.

A designer finishes a style in CLO. The render looks great. The work is done, you might think. This is where for many the least interesting part starts: saving, renaming files, and uploading them into different systems.

This is one example from one brand running thousands of styles a year. But it’s not limited to that brand, or to teams working in 3D. We see the same pattern everywhere - whether you design in Illustrator, with AI, or in CLO. Digital design tools create excellent output, but how much your company benefits from it fully depends on how well your tools and data are connected.

Turn tools into a workflow

There is no one-size-fits-all. Every team has a different calendar, different naming conventions, different places to store assets, and yes, different habits. To fully understand where automation can have the most impact with the least effort, we measure the workflow. We follow individual users, see where they click, what tasks they do manually, and ask where they experience the most friction. That heatmap tells us where we can have most impact.

FOCUS

As you might already sense, most of the friction sits in the handover between tools. That’s where users experience the most disruption. And honestly, why would a designer or merchandiser need to become an expert in naming conventions, render settings, or prompt writing?

One of the most powerful tools in our toolbox is FOCUS. It sits at the centre of the workflow and links tools based on brand milestones and context. At the point of saving a design from Illustrator, CLO, or any AI tool, the user gets a simple pop-up asking for the season, style data, and milestone. Based on your workflow, FOCUS takes over everything you would normally do manually. Saving, naming, rendering, combining AI and 3D, sending images and data to PLM or DAM. All in one click.

What got connected

CLO feeds into FOCUS. FOCUS triggers CLOSET. CLOSET pushes to PLM. PLM syncs to Miro. The automation layer handles the data flow, formatting, enriching, and delivering to each tool with exactly the right context, every time.

No manual export or navigation required. And when you decide to change tools the automation can easily be adapted even without changing the user experience.

The impact

85% reduction in manual work. Not from replacing the tools. Not from buying something new. From connecting what was already there and removing the steps that shouldn’t exist. The designers didn’t change how they work in CLO. They just stopped doing everything that came after it.

This is what a workflow assessment gets you

A workflow assessment doesn’t only show you where you can have the most impact, it also saves you building integrations that won’t pay off. Our model has been developed and tested with large enterprise teams, and gets more reliable with every engagement. We map it before we build anything. That’s always the order.

First we find where time is lost. Then we identify what should connect. Then we build the automation that makes the manual work disappear.

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On the FERMAT Podcast - why digital transformation stalls at the point of decision, and what it takes to connect product data, digital creation and AI into infrastructure that actually holds.

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