Key highlights
Live in ~1 week
Pletor integrated Polotno into its Composer feature fast enough to start testing the first version with customers.
~10k renders/mo
The team renders composed assets through Polotno Cloud Render from its own frontend and workflow builder.
Enterprise-ready
Polotno lets Pletor give enterprise customers control over images, text, layouts, and brand elements after AI generation.
Company
Pletor is a French startup helping brands produce visual assets more efficiently. The platform is built for marketing teams — including brands like Comptoir, Revolut, and Carrefour — that need more creative output than a traditional design workflow can comfortably handle. Instead of producing every asset manually, teams build workflows that combine AI models, brand logic, and repeatable creative rules. Pletor had already built the AI and workflow side of the product; what they needed next was a practical editing layer that could sit inside those workflows and make generated assets easier to control before final rendering.
Challenge
Pletor needed to bring structured editing into an AI-first production system. Generative AI created raw visual material, but final assets still had to respect brand and campaign rules — existing logos, approved fonts, fixed layouts, market-specific text, and predictable output across many variations. Prompting alone wasn't reliable enough, and manual cleanup defeated the purpose of repeatable workflows. Building the editing layer in-house also meant building a rendering engine, a reliable design data model, and production-grade infrastructure — a costly detour for a lean team.
Solution
Pletor chose Polotno as the foundation for its Composer feature. Composer lets users take generated media and define how the final asset is assembled: Pletor controls the product experience and builds the design structure on the frontend, while Polotno Cloud Render handles backend rendering. A customer can generate an image, place a brand element on top, define the layout, and reuse that setup across many variations — the generated media changes from one output to the next while the composition rules stay controlled.
The challenge
AI generation still needed a controllable final step
Pletor's product is built around workflows. Users create automations from different nodes, similar in spirit to products like Zapier or n8n. Some nodes generate media, some handle logic, and some prepare outputs for use elsewhere.
As the product moved toward larger marketing teams, Pletor saw a recurring need for what Antoine, the company's CTO, calls "last mile editing." Customers want AI-generated assets, but they also want to decide what stays consistent. A campaign might need different images, copy, or language versions while following the same visual structure. In another case, the logo might change by country or brand, but its placement should stay controlled. That level of consistency is difficult to get from AI generation alone.
Pletor could have built an internal editor, but the scope went deeper than it first looked. The product needed rendering, reliability, and a way to use design data programmatically inside automated workflows. The backend rendering part mattered most — the editor had to become part of the product infrastructure, not an isolated design tool. Polotno made the buy-versus-build decision easier by covering the editing and rendering layer well enough that Pletor could focus on its own workflow product.
“The cool thing about generative AI is you can generate a lot of things, but sometimes you need more consistency or last mile editing.”
Antoine Sueur
CTO @ Pletor
The solution
Creative editing layer inside Pletor
Pletor integrated Polotno into its stack. The Composer takes inputs from the rest of the workflow and assembles the final media output — users add generated images, place brand elements, adjust the composition, and render the result.
The first version shipped in about a week. After that, the team improved the integration around reliability, edge cases, retry logic, and production behavior. Technically, Pletor uses its own frontend to define the right JSON structure, then sends that data to Polotno Cloud Render. Polotno doesn't have to be a separate destination for users — it works as part of the Pletor product.
That mattered because Pletor's customers aren't only editing one asset at a time. They often want to set up a workflow once and run it across many inputs. A team can create a campaign workflow, generate a group of product visuals, and use Composer to apply the same layout logic across all of them — keeping control of the final asset without turning it into repetitive manual editing.
As Pletor expanded into video, Polotno became relevant there too. The team started building features for trimming, speed adjustment, sequencing, audio handling, and combining several videos into one output. The goal isn't to replace a full professional video editor, but to give users enough control to create useful marketing videos inside the same automated workflow where the assets are generated.
“It's always a sort of buy or build dilemma. We looked into it. It's quite a deep product. And the main issue is mainly on the backend side, because you need a sort of render engine. This is not something we wanted to put the effort into, and Polotno was a great solution for that.”
Antoine Sueur
CTO @ Pletor
The outcome
Pletor added editing without turning it into a major internal build
Polotno helped Pletor add a feature customers needed without pulling the team into months of editor and rendering infrastructure work. The first version of Composer was live in roughly a week, which let the team test the idea quickly and improve it through real product usage.
Pletor now runs around 10,000 renders per month through Polotno Cloud Render. Rendering has generally worked well, with the main production work coming from handling occasional timeouts, edge cases, and reliability questions around the Composer workflow.
For the business, the impact is less about one isolated metric and more about product fit. Pletor serves larger marketing teams, and those teams need generated creative to stay usable inside a brand system. Polotno gave Pletor a way to meet that expectation without building the entire editing stack internally.
It also gave the team room to keep expanding. The same Composer concept that started with images is now moving into video, where Pletor is building more control over clips, audio, timing, and branded elements. As Antoine described it, Polotno was a good fit because it was easy to integrate, worked well in the proof of concept, and let Pletor scale the cost as usage grew.
