What Madoo is
Madoo is a workflow engine for producing and transforming content: images, video, audio, 3D models, text and documents. You describe a piece of production work once — as a workflow — and Madoo runs it for you: on one item or on thousands, today or in a year, with the same steps, the same rules and the same kind of result every time.
Madoo is not a single AI tool. It orchestrates many of them — image, video, audio, speech and language models from several providers — together with a large set of deterministic processing steps that involve no AI at all: resizing and cropping, color and text overlays, video trimming and merging, audio mixing and loudness, subtitles, PDF documents filled from data, spreadsheets, validation and routing. Most real production work needs both.
Why Madoo exists
Section titled “Why Madoo exists”Producing content with AI looks easy for one image and becomes hard as soon as it is real work. Madoo was built around five problems that appear every time:
- Complexity. Real content is rarely one model call. A product sheet needs a cut-out photo, a generated setting, copy in two languages, a price from a catalog and a PDF layout. A localized video needs a transcript, a translation, timed captions, a synthetic voice that fits the timing, and a final mix. Madoo lets you express that chain as one workflow, with every step visible and inspectable.
- Repeatability. A good result obtained by hand in a chat cannot be replayed on the next product. A workflow captures the process — models, prompts, parameters, rules — so the same process runs again on new inputs. A published workflow is versioned and does not change under you.
- Elasticity. The same workflow runs on one item or on a whole catalog. Lists fan out into parallel work and are collected back into one result (a CSV, a JSON, a multi-page PDF, a merged video) — see Iteration.
- Scalability. Executions run on a pool of workers, in parallel, with retries, cost limits and credit accounting per step. Adding volume does not add people.
- Integrability. Content is needed inside other systems — a shop, a CMS, a mobile app, a back office. Every workflow can be run through an API, from an AI assistant, inside an embedded widget or as an App, so Madoo can be the content engine behind someone else’s product.
The workflow is the unit of work
Section titled “The workflow is the unit of work”Everything in Madoo revolves around the workflow: a graph of nodes connected by typed ports.
- Input nodes receive what changes from one run to the next: a photo, a video, a text, a number, a CSV or JSON dataset, a list of values.
- Processing nodes do the work. Some call AI models (generate or edit an image, write copy, transcribe speech, synthesize a voice, animate a picture); many are deterministic (resize, crop, trim, merge, mix, render a document template, validate JSON, filter, pick a branch).
- Output nodes name what the run returns: an image, a video, an audio file, a text, a JSON object, a CSV, a PDF, a 3D model.
A workflow is authored as a draft, checked by validation, published as an immutable version, and then executed. Each execution records what every node did, what it produced and what it cost. The catalog has around two hundred node types; how they combine is explained in How workflows are built.
Because the same workflow can be run with different inputs, it behaves like a function: the inputs are its parameters, the outputs are its result. That is what makes it reusable by people, by programs and by AI agents alike.
AI and deterministic processing, together
Section titled “AI and deterministic processing, together”Madoo treats AI and non-AI steps as equals in the same graph, and that is deliberate:
- AI where judgment or creation is needed — generating an image, rewriting copy for a market, choosing the best moments of a video, translating captions, producing a natural voice.
- Deterministic steps where exactness is needed — the price printed on a flyer must be the one in the catalog, a caption must end when the speech ends, a PDF must follow the brand template, a video must be exactly 1080 × 1920. These steps give the same output for the same input, cost little or nothing, and make AI output safe to use in production.
A good workflow often lets AI propose and deterministic steps decide: an AI node writes a JSON answer, a schema validation checks it, a predicate routes it, and a template renders it with facts that never passed through a model. Credits are spent only where AI or heavy processing is used, and every run can be estimated before it starts.
Many ways to author and run the same workflows
Section titled “Many ways to author and run the same workflows”A workflow is the same object whichever way it is built or run. Madoo exposes it on several surfaces, and all of them matter because they serve different people:
| Surface | Who uses it | What it is for |
|---|---|---|
| Visual editor | Designers, creative technologists, developers | Build and inspect workflows on a canvas: nodes, connections, parameters, test runs, execution history. |
| Document template editor | Designers | Design the page layouts (brochures, sheets, certificates, posters) that workflows fill with data and render as PDF or images. |
| Madoo AI — the built-in agent | Non-technical users | Describe the goal in plain language; the agent finds or builds the workflow, explains the cost, runs it and can turn it into an App. |
| Apps | Anyone | A simple interface over one or more workflows: fill a form, upload a file, get the result — without opening the editor. |
| Public API (REST) | Developers | Run, author, validate and publish workflows and templates from any backend. See Public API and MCP. |
| MCP server | AI assistants and coding agents | Connect Claude, ChatGPT, Codex, Cursor and other assistants: they can discover nodes, build workflows and templates, run them and read the results. See MCP server. |
| Embeds, batches, webhooks | Integrators | Put a workflow or the editor inside another product, run thousands of items as a batch, get notified when a run ends. |
These surfaces are kept at parity: a workflow built by an agent over MCP opens in the visual editor, a workflow drawn in the editor can be run over the API, and the same rules validate all of them.
Madoo as the content engine of an application
Section titled “Madoo as the content engine of an application”A large share of the value of Madoo is in what others build on top of it. Two ways of building software make this especially direct:
- Coding agents (Claude Code, Codex and similar) connect to Madoo over MCP, build and test the workflows an application needs, and write the application code that calls them through the API.
- Vibe-coding platforms (Lovable and similar) connect to Madoo as a backend for content: the app provides the user experience, Madoo provides the production pipeline — image generation, video localization, document rendering — with credits, limits and results already handled.
In both cases the workflow is the contract between the application and Madoo: the application sends inputs and reads outputs; how the content is produced can evolve inside the workflow without changing the application.
For people who do not write code, Madoo AI plays the same role: it lets a non-technical user set up a content production pipeline — including AI steps — by describing it, and hand it to colleagues as an App.
What Madoo is used for
Section titled “What Madoo is used for”Madoo is multi-domain. A few examples of work that fits a workflow:
- E-commerce and retail — product photos cut out and placed in settings, descriptions in several languages, product sheets and catalogs as PDF.
- Marketing and advertising — campaign visuals in every format, localized copy, flyers and posters from a brief and a product list.
- Publishing and education — illustrated summaries, certificates for every participant of a course, documents assembled from data.
- Real estate and hospitality — listings built from a photo shoot and a property sheet, brochures from room data and photos.
- Video and audio — subtitles and translated captions, dubbing with natural timing, voice clean-up and audio finishing, highlights cut from long recordings, screen recordings turned into polished videos.
Where to go next
Section titled “Where to go next”- How workflows are built — nodes, ports, the lifecycle of a workflow and what happens when it runs.
- Iteration — how one workflow processes a list of items, and how the results are collected back.
- Public API and MCP — integrating Madoo into a product or connecting an assistant.