Inner AI Skips the Blank Chat Box – You Start With a Template

01 /The Concept

Most AI tools start with a blank text box. Inner AI starts with a catalog of things you can make right now – no prompting required. That design decision sounds small, but it’s the whole product.

When users open the platform, they see a catalog of things they can make – immediately, without navigating a blank chat interface. That's because the platform ships with a library of ready-made templates covering the most common work tasks. Company administrators can also create and add custom templates for anything specific to their workflow.

Out of the box, the templates support reports, sales proposals, onboarding curricula, process documentation, blog articles, job descriptions, ad campaigns, customer support responses, and more.

While creating any content, users can ask the AI to pull data from internal corporate sources connected to Inner AI – for example, when drafting an employee handbook, the AI can retrieve the most common questions from the company's internal chat and draft answers alongside them.

Generated text is editable through natural-language instructions: make this paragraph shorter, simpler, more persuasive – selected from a menu. The same applies to images in documents: change the background, adjust a color, modify an expression. The platform also generates videos, editable simply by changing the script that a character speaks.

Collaboration is built in: documents can be shared with others for co-editing or comments.

Under the hood, Inner AI runs models from OpenAI, Mistral, Stable Diffusion, and others. Administrators select which models handle which task types. Individual pricing starts at $12/month; enterprise pricing is on request.

Inner AI was founded in Brazil last year, closed a $2.4M seed at launch, shipped the product, signed its first clients (including well-known Latin American companies), and has now raised another $2.4M.

02 /Why It Matters

Most people's default interaction with AI is to open ChatGPT and type something. That's fine for one-off questions or novel tasks. But using AI as a systematic business capability means embedding it inside recurring workflows – not training employees to write prompts for various chat interfaces.

That's the problem Inner AI decided to solve: rethink how AI-assisted content creation actually works inside a company, and rebuild it from scratch.

The startup hopes users will look back and wonder how they worked without it.

Zylon, [covered previously](/review/vzleti-na-kryljah-interfejsa), is heading in the same direction. It raised $3.2M for a beta of its platform that automates routine employee tasks using AI. Like Inner AI, it's a task-selection interface that skips the chat step – users click on what they want to do and follow guided prompts. Zylon's founders argue that AI "shouldn't feel like magic" – expecting a single button press to produce a perfect output. It's faster and more reliable to go step by step, feeding in information incrementally toward a predictable result. Inner AI's templates serve the same function.

Twin, [covered here](/review/kupjat-potomu-chto-nekomu-jetim-zanimatsja), is also building task automation for small and mid-size businesses through AI. Their primary interface is still a chat, but they're purpose-training the AI on common business task types and currently onboarding first business partners. The platform isn't live yet – but they've already raised $3M.

03 /Opportunities

There's an old saying: "Every revolution is conceived by romantics, executed by fanatics, and enjoyed by opportunists." In technology it's less dramatic, but the structure holds: inventors create the technology, engineers build it into products, and skilled operators build the businesses on top.

Most founders want to be the inventors. They dismiss products built as "wrappers" around AI models because the ambition is to build the models themselves.

Inner AI and the other startups mentioned here are, in a literal sense, wrappers. But that doesn't make their products less valuable – it may actually make them more useful. Because 99% of users don't want the technology. They want a simple, effective tool that solves a specific problem.

The general direction: take existing AI technology and adapt it for specific recurring tasks in specific domains.

Inner AI, Zylon, and Twin are all going after a fairly broad category – general business process automation for SMBs.

But the applications can be narrower and more surprising. Fora, [covered here](/review/pust-i-menshe-no-zato-dorozhe), built an AI platform specifically for C-suite executives and raised $3.8M at pre-seed. The narrower the niche, often the stronger the positioning – and sometimes the higher the price.

Which business processes could you simplify with existing AI technology? How should the workflow be structured and broken into steps? How does it need to integrate with other tools the company already uses? What should the interface actually look like?

Don't invent new technology. Use what's already been built. That's often the stronger position.