Introduction

Artificial intelligence is sweeping through enterprises, from AI-driven analytics to customer support bots. Yet a major obstacle remains: the user interface. Many internal tools - the custom dashboards, forms, and apps employees use every day are still built the traditional way, with static screens and hardcoded workflows. This disconnect between AI’s capabilities and a rigid UI is holding back adoption of AI in the enterprise as I discuss in What is Generative UI. According to Boston Consulting Group, 74% of companies have yet to see tangible value from their AI initiatives, largely because users don’t fully adopt tools that feel confusing or irrelevant to their needs (BCG, 2024). In other words, even the most powerful AI solution can flop if presented through a clunky interface. Teams can spend months building an internal app’s frontend, only to deliver a one-size-fits-all experience that users find underwhelming. The rise of large language models (LLMs) calls for a new approach to frontends one as adaptive and intelligent as the AI back-end.

I discuss in What is Generative UI that Generative UI is emerging as that next step. Generative UI (short for Generative User Interface) refers to UIs that are dynamically generated by AI in real time, rather than designed entirely in advance (AI Native Frontends). In a generative UI system, the interface can essentially build itself on the fly based on the AI’s outputs and the user’s context, instead of being fully hardcoded. This concept promises to transform how internal tools are built and used across the enterprise. Instead of static forms or dashboards that change only when developers push an update, a generative UI can adapt on-demand presenting exactly the components or data a user needs at that moment. From dynamic AI UI assistants to on-the-fly AI dashboard builders, generative UIs enable a new class of AI-native software where the frontend is as flexible as the AI logic behind it. In this post, we’ll explore how generative UI works, the benefits of these LLM-driven product interfaces for internal tools, and how developers can build UI with AI. We’ll also look at the role of C1 by Thesys in making generative UIs practical for enterprise teams. By the end, it will be clear why GenUI (Generative UI) could be the key to turning raw AI power into intuitive, dynamic user experiences for employees and customers alike.

The Traditional Role of Internal Tools (and Its Limitations)

Every organization relies on internal tools software applications used in-house to support operations and decision-making. These range from administrative dashboards and CRMs to customer support consoles, inventory management UIs, data analytics dashboards, and custom workflow tools. The goal of internal tools is to streamline business processes by automating tasks, connecting data sources, and providing user-friendly interfaces for employees. A well-designed internal tool can save hours of work by allowing non-technical staff to interact with data and systems easily, whether it’s updating a customer record or pulling a sales report.

Traditionally, internal tools have been built either by engineering teams from scratch or by using low-code platforms. In both cases, the UI is typically predefined and static. Developers or product managers gather requirements, then design forms, tables, buttons, and charts that anticipate the users’ needs. This interface is usually hardcoded it will only display the screens and options the designers thought of in advance. If a new requirement or use case emerges, the tool’s UI has to be manually updated or a new tool is built. This frontend development cycle for internal tools can be slow and costly. Enterprise dev teams often face backlogs of requests for new internal dashboards or tweaks to existing ones. Each change adding a new form field, creating a new report view might require days or weeks of coding and QA. The result is that internal tools often lag behind the evolving needs of the business.

Moreover, static UIs can make internal workflows rigid. Employees must adapt their tasks to what the software expects, rather than the interface adapting to the task at hand. For example, a customer support agent might have to click through several fixed screens to gather information from different systems, copying and pasting data between forms. An analyst might export data to Excel because the built-in dashboard can’t visualize it the way they need. These pain points arise because traditional interfaces are not very adaptive. They present the same layout of fields and buttons to every user, every time, even if today you only need half of them. In an era when AI can tailor recommendations and automate decisions, having a rigid UI is starting to look like a bottleneck.

This limitation becomes especially acute with AI-powered internal tools. Imagine an internal chatbot or an AI agent that can answer complex questions or perform actions. If the interface for it is just a basic chat box or a generic form, users may not leverage the AI’s full capabilities. In fact, building a user-friendly frontend for AI agents has been a “major hurdle” for teams adopting AI. Many current AI applications in enterprises default to bland web forms or text outputs, limiting the promise of these advanced systems. The static UI not only reduces user engagement but can also make the AI’s outputs harder to understand (for instance, showing a complicated analysis as a dump of text instead of a clear chart).

In summary, internal tools are essential for operations, but how we build their UIs hasn’t changed much in decades define requirements, code the interface, repeat for every update. This manual approach struggles to keep up with fast-changing needs and the dynamic nature of modern AI. This is where generative UI offers a compelling alternative.

What is Generative UI (and Why It Matters)?

Generative UI (GenUI) refers to a new approach where the user interface is generated by AI models on the fly in response to user input and context, rather than being entirely pre-built by developers (What is Generative UI). In practical terms, generative UI lets an AI (such as an LLM) create or modify the frontend elements of an application in real time. The interface becomes malleable and context-aware. If the user’s needs change or if the AI’s output suggests a different presentation, the UI can update itself without a human deploying a code change.

Why is this so powerful? Because it addresses the very rigidity we discussed in traditional tools. With generative UI, the software can adapt to each user and each moment. Instead of every user seeing the same fixed dashboard or form, the interface could be different for each scenario, generated on demand by the AI. For example, if an internal AI assistant is helping an employee troubleshoot an IT issue, it might initially present a text chat. But the moment it detects the user’s problem involves, say, checking system status, the AI could generate a dashboard UI with status indicators and logs. If the user then asks a question that requires additional input (like scheduling a maintenance window), the AI could conjure a form with the necessary fields. Once the issue is resolved, the interface might transform again to show a summary report. All of this happens in real time, driven by the AI’s understanding of the conversation and context.

Contrast that with a traditional tool: the user would be stuck navigating whatever screens were built beforehand, even if those screens aren’t exactly what’s needed for the current situation. Generative UI turns the frontend into something alive and context-aware, not a static set of screens (AI Native Frontends). It’s similar to how a great human assistant would lay out exactly the papers or tools you need for the task you’re doing, then switch them out when you move to the next task. Here the “assistant” is an LLM, and it can create UI elements on the fly to help you.

Crucially, generative UI is about the AI going beyond just text outputs. Large language models are brilliant at generating natural language, but internal tools often need more than text - they need buttons to click, charts to visualize data, tables to edit records, etc. Generative UI enables the AI to present those richer interactions. As one developer-focused article put it, an AI assistant shouldn’t be limited to replying with plain text; it could generate a chart or a form if that best answers the user’s request (What is Generative UI). In fact, an LLM-based system could assemble an entire mini-app or LLM-driven product interface on demand. If a manager asks an AI for “show me the top sales opportunities and let me update their status,” a GenUI-powered tool might create a custom dashboard with a table of opportunities and editable fields, essentially acting as an AI dashboard builder for that query. No one had to pre-design that interface - the AI built it because it knew what the user needed and it had the building blocks to construct it.

For internal enterprise tools, this is a game changer. It means internal software can finally keep up with the complexity and variability of real business processes. Every employee could have a slightly different, personalized interface tuned to their role and the task at hand, generated in real time. This level of personalization at scale was impractical with traditional UIs, but becomes feasible when an AI is creating the interface dynamically (What is Generative UI). Generative UI essentially makes the software adaptive, which is incredibly important when working with AI systems. As AI logic and data change, the UI can change along with it, staying in sync. This tight AI-UI integration is why generative UI is often called a cornerstone of AI-native software design - it ensures the user experience is as advanced and adaptable as the AI behind the scenes.

How Generative UIs Work: LLM UI Components and Frontend Automation

So how can an AI actually generate a user interface? It’s not sketching pixels from scratch - under the hood, there is a framework that makes generative UI possible. The key idea is to leverage LLM UI components. Essentially, developers define a set of components or frontend elements (charts, tables, text inputs, buttons, forms, etc.) that the AI is allowed to use. Each component is like a template or widget that the AI can plug data into. When the AI wants to present something visually, it doesn’t draw it pixel-by-pixel; instead, it outputs a structured description that says “use component X with these parameters/data.”

For example, imagine the AI is asked to show sales data by region. Rather than returning a paragraph describing numbers, the LLM could output a JSON or other structured payload like:

arduinoCopy{ "component": "Chart", "title": "Sales by Region", "data": [ ... ] }

The application’s front-end recognizes that and renders an actual chart in the UI with the provided data (AI Native Frontends). If the user then asks to filter that data, the AI might output a specification for an interactive filter component or a new chart. In another case, if the AI needs more input from the user (say, to schedule a meeting), it could output a definition for a form: e.g. “{ "component": "Form", "fields": [ {name: 'Date', type: 'date'}, ... ] }”. The front-end would then display a date picker and other fields. These LLM UI components act as the bridge between the model and the interface - the AI produces a high-level description, and the front-end code knows how to render it as actual UI elements the user can interact with.

This approach requires an orchestrating system often called an AI front-end API or generative UI runtime. Developers integrate this into their application so that it can interpret the model’s outputs and map them to real UI updates. Frameworks are quickly evolving to support this pattern. For instance, the open-source CopilotKit library lets an AI agent in a React app “generate completely custom UI” by linking LLM outputs to React components (Deshmukh, 2025, AI-Native Frontends). The popular LangChain framework, known for LLM “agents,” has introduced features for streaming LLM outputs directly as components in a web interface (Deshmukh, 2025, AI-Native Frontends). Even mainstream AI platforms are moving this direction: OpenAI’s ChatGPT now has function calling and plugin capabilities, which essentially let it output data in a structured way or trigger UI-like elements instead of just raw text (AI Native Frontends). All of this is aiming at the same goal - frontend automation, where we move beyond automating code generation to automating the generation of the interface itself (What is Generative UI).

To put it simply, generative UI works by giving the model the ability to pick and choose UI elements the same way it would choose words in a sentence. The developer’s role shifts from writing all the UI code to orchestrating the AI: providing the library of components, establishing guidelines (via prompts or system instructions) for when to use which component, and handling security or validation. For example, a developer might prompt the model with: “You can answer with these UI components when appropriate: Chart, Table, Form, etc. Use Chart for data visualization,” and so on. The model then includes those components in its output when relevant. The frontend API receives the model’s response, sees a “Chart” component instruction, and knows to render the chart.

This paradigm still ensures human control and safety. The AI isn’t executing arbitrary code - it’s limited to calling predefined components that developers have vetted. The interface can be as rich or as constrained as the designers decide. One enterprise might allow their generative UI to output complex dashboards and multi-step forms; another might restrict it to only a handful of safe components. In any case, the result is a system where adding a new feature to an internal tool might be as simple as adding a new component to the palette and telling the AI about it, rather than writing a bunch of new UI screens from scratch.

The emergence of tools like C1 by Thesys exemplifies how this works in practice. C1 by Thesys is a Generative UI API that uses LLMs to generate user interfaces on the fly, turning model outputs into live, dynamic UI elements in real time. With an API like this, developers send user prompts and context to C1 by Thesys, and instead of just getting text back, they get interactive UI components back. For instance, Thesys reports that Generative UI can interpret a natural language request, produce contextually relevant UI components, and adapt those components dynamically as the user interacts or as the state changes. In effect, C1 by Thesys provides that runtime which maps the LLM’s decisions to actual interface elements (through a React-based rendering engine). This allows enterprise teams to plug generative UI into their existing tech stack - the AI handles a lot of the front-end logic, but teams can integrate it without overhauling everything. As Thesys noted in their launch, C1 by Thesys integrates with modern frameworks and lets you adopt generative UI without rebuilding your whole app from scratch.

Benefits of Real-Time Adaptive UIs for Internal Tools

Adopting a generative UI approach can bring significant benefits for both the developers of internal tools and the employees who use them as I mention in What is Generative UI. Here are some of the key advantages of having real-time adaptive UIs powered by LLMs:

Personalization at Scale: Generative UIs can tailor themselves to individual users’ needs and preferences without manual configuration. The interface one user sees could be completely different from another’s, because it’s generated on the fly to suit each scenario. Every employee, in every session, gets an interface “tailored just for you, in that moment,” to quote Thesys’s founders. This level of personalization was impractical with traditional static UIs, but becomes achievable when an AI creates the UI dynamically (AI Native Frontends). For an enterprise, this means roles as diverse as customer support, sales, and operations can all use the same AI-driven tool and each get a UI adapted to their context.
Real-Time Adaptability: Because the UI is generated in response to context, it stays in sync with the underlying AI’s capabilities and the user’s goals. The interface can evolve instantly as the situation changes. If the data updates or the user shifts direction, the UI morphs to follow. This is crucial for internal tools that deal with live data or iterative workflows. Users get an interface that adapts as they interact, rather than hitting dead-ends or having to request a new feature and wait for the next software update (AI Native Frontends). In fast-paced business scenarios, this kind of fluid UI ensures the tool is always aligned with the task at hand.
Faster Development & Iteration: For developers, generative UI promises huge gains in efficiency. Much of the tedious “glue code” and boilerplate UI work is offloaded to the AI. Companies adopting generative UI have found they can roll out new features or interface changes much faster, since the AI handles the heavy lifting of UI updates. Routine interface adjustments like adding a new form field or supporting a new data view - no longer require weeks of coding; the AI can generate what’s needed on the fly guided by high-level prompts or rules (AI Native Frontends). This dramatically shortens development cycles and lets teams iterate rapidly based on user feedback. In essence, developers can focus more on defining what should happen and let the AI figure out how to present it in the UI.
Reduced Maintenance & “Glue” Code: Maintaining internal tools can be a major drain on engineering resources - every time a process changes or an API response changes, someone might have to tweak the UI. Generative UI can significantly reduce this burden. Instead of constantly tweaking UI code to keep up with changing requirements, developers can concentrate on refining the AI’s logic and the available components. The AI front-end system (like C1 by Thesys) handles many of the UI adjustments automatically (AI Native Frontends). This also means fewer integration bugs, since there are fewer manual hand-offs between back-end outputs and front-end displays; the AI is orchestrating that connection. Over time, this frontend automation leads to leaner codebases for internal tools and less technical debt.
Improved UX and User Engagement: A dynamic, AI-driven interface can provide a far more intuitive and engaging experience for end users (employees). Instead of forcing users to navigate through a rigid menu or input data in a generic form, the UI can present information in the most suitable format and even guide the user proactively. For instance, the generative UI could display a chart, map, or interactive widget when it’s the best way to convey information, or it could provide step-by-step UI assistance for a complex task. Users can also interact via clicking buttons, adjusting sliders, or filling forms that the AI generates for them, rather than having to craft perfect text queries all the time. This multimodal interaction builds trust and clarity - users see the AI’s actions and reasoning in a visual way, which makes the AI less of a “black box” and more of a collaborative helper (AI Native Frontends). Overall, an internal tool with generative UI can drive higher adoption because it feels responsive and intelligent, adapting to help the user succeed.
Scalability and Future-Proofing: As your AI systems evolve with new capabilities or as your internal processes change, a generative UI can scale alongside. You won’t need to redesign the interface from scratch to expose a new AI feature; the LLM can incorporate new types of outputs into the UI as soon as it knows how to describe them. This makes your internal tools more adaptable to future requirements and ensures the user experience can continuously improve without waiting for big front-end releases (AI Native Frontends). In an environment where AI technologies and business needs are advancing rapidly, having a UI that can keep pace dynamically is a strategic advantage. It means your investment in AI won’t be held back by a static interface - the UI layer will be as flexible as the backend, ready to leverage new data sources or model improvements immediately.

In sum, generative UI helps align the user experience of internal tools with the full power of modern AI. It turns what could be a static, one-size-fits-all interaction into something engaging and continuously optimized. For developers and product teams, it enables a new level of agility in delivering features, while for end-users it provides interfaces that essentially “work with you” - changing as needed to best achieve the task.

Building Internal Tool UIs with AI: From Prompts to Interfaces

A natural question arises: how can developers practically build UIs with AI? What does an AI-driven development workflow look like for internal tools? It’s important to note that generative UI doesn’t eliminate the need for developers or designers - instead, it changes their focus. Here’s how teams are beginning to build generative UIs in practice:

1. Define the Component Toolbox: First, developers decide what UI components the AI can use. Think of this as defining the vocabulary for the AI in the UI domain. Common components might include charts, graphs, tables, lists, forms, text blocks, images, buttons, and modals. For internal tools, you might also have domain-specific widgets (e.g. a ticket view component for an internal ticketing system, or a customer profile card). Each component is implemented in your frontend stack (for example, a React component library) and has a clear interface for data/parameters.

2. Establish the API/Integration: Next, integrate an AI frontend API or framework (like C1 by Thesys or similar) into your application. This layer will handle communication with the LLM and the rendering of components. For instance, C1 by Thesys provides an API where you send the conversation or user prompt, and it returns a response that could include structured UI outputs. Your application then takes that response and, via the integration library, maps it to actual UI updates in the user’s browser. This is typically done with a lightweight client-side library that knows how to take a JSON or function-call output from the LLM and invoke the corresponding UI components.

3. Prompt Engineering for UI Generation: With the groundwork laid, developers craft prompts and instructions for the LLM so it knows when to generate which UI. This might involve system prompts that describe each component’s purpose and usage. For example: “If the user asks to visualize data, you can return a Chart component. Use a Form component if you need to ask the user for additional info.” Essentially, you train or prompt the model to be a sort of UI agent that can decide how best to present information. There is a bit of art and science here - teams often iterate on prompts or use few-shot examples to teach the model the right outputs. Over time, as the model’s capabilities improve (or if fine-tuned on your domain), it gets better at deciding on helpful interfaces.

4. Iterative Development and Testing: Building UI with an AI in the loop involves a different testing approach. You’ll test not just if the UI components work, but if the AI chooses appropriate components and data. Tools like C1 by Thesys allow developers to specify constraints (so the AI doesn’t produce unsupported components or too many elements at once, for example). During development, you might simulate various user queries and see how the AI responds - does it generate a sensible UI structure? Are the responses concise and within allowed formats? This is analogous to testing an AI’s text output for quality, but now you check the structured output. Many teams use a playground environment (Thesys provides one for C1 by Thesys) to prototype prompt changes and see UI results immediately.

5. Safeguards and Controls: In an enterprise setting, governance is key. Developers will set up validation on the AI outputs. If the AI tries to generate an interface that is not allowed or doesn’t make sense, the system can either sanitize it or reject it. For instance, if a rogue prompt made the AI output 100 table columns, you might have a rule to limit component complexity. Similarly, sensitive data handling can be enforced at this layer - e.g., if the AI tries to show customer PII in a UI, perhaps the integration layer checks that against permissions. So while the AI is “writing” part of your UI, it’s doing so in a controlled sandbox defined by the dev team.

6. UI/UX Design Collaboration: Interestingly, generative UI development can become a more collaborative process with UX designers as I discuss in What is Generative UI. Instead of handing off static mockups, a designer could specify design guidelines that the AI should follow (like preferred color schemes for components, or layout constraints). Since the AI can be guided by high-level rules, designers might input style prompts or templates that the AI’s outputs should conform to. We are still in early days, but one can imagine design tools where a designer can tweak a component’s appearance and the AI will use that style whenever it generates that component. This merges the line between design and implementation - a lot of the design intent can be embedded in how the AI generates UIs.

Overall, building UI with AI means thinking in terms of patterns and possibilities rather than fixed pages. Developers set up the building blocks and guardrails; the AI assembles those blocks in real time. This is a paradigm shift, but one that can significantly speed up internal tool development. It also makes it easier to maintain and evolve tools - to add a new “feature”, you might add a new component and teach the AI about it, rather than building a whole new UI flow by hand.

Internal Tools Reimagined: Examples of Generative UI in Action

To make this concrete, let’s walk through a couple of scenarios showing how generative UI could transform internal tools:

Example 1: AI-Powered IT Support Dashboard - Consider the internal tool used by an IT helpdesk team in a large enterprise. Traditionally, they might have a dashboard where they search a knowledge base, fill out forms to create tickets, and maybe a chat window for an AI assistant. With generative UI, this could become a single LLM agent user interface that fluidly changes based on the support request. Suppose an employee asks via chat, “My laptop is running slow, what should I do?” The AI might start by responding with a few troubleshooting steps in text. As the conversation continues, it realizes it would help to show a system status. Suddenly, the UI expands to display a dynamic chart of the laptop’s CPU and memory usage that the AI fetched via a monitoring tool integration. The user doesn’t have to open a separate monitoring app - the AI exposed that data right in the chat. Next, the AI needs some info from the user to proceed (like the laptop asset ID), so it generates a form with a field for the asset ID and perhaps a drop-down for selecting the device (populated from company inventory data). The user fills it and submits, and the AI continues with the diagnosis. Throughout this, the interface has been changing in real time: from chat messages to charts to forms, all within one unified tool. At the end, the AI might even produce a “Fix it” button if an automated remediation is available - the agent can click it, and a script runs to clean up the laptop’s processes. This kind of dynamic UI with LLM makes the support workflow much more efficient. The agent doesn’t have to juggle multiple systems; the AI brings the needed UI elements to them. It feels less like using a dozen tools and more like having a conversation with an expert that also hands you the right tools exactly when you need them.

Example 2: Adaptive Sales Operations Dashboard - Now imagine a sales operations manager who typically uses several internal dashboards to track leads, revenue, and team performance. In a generative UI world, they could have an AI-driven dashboard where the manager simply asks questions or gives commands, and the UI assembles itself accordingly. For instance, the manager asks, “Show me this quarter’s top 5 deals with their status, and let me update the forecast.” In a traditional tool, the user would manually navigate to a reports page, filter by quarter, find top deals, then click into each deal to update fields. With generative UI, the AI interprets the request and generates a custom view: a table listing the top 5 deals (by value) and their status, along with editable fields or an “Update Forecast” button next to each. This is essentially an AI dashboard builder at work - the AI created a mini-dashboard on the fly for that specific query. The manager can interact directly: maybe they click to adjust the expected close date on one deal via a date-picker that the AI provided as part of the interface. Next, the manager asks, “What about regional performance? Show a breakdown by region.” The AI might replace or augment the table with a chart component, because a chart is a better way to see regional breakdown. The UI transitions smoothly into a bar chart of revenue by region. If the manager sees something off and types, “Drill into APAC,” the AI could zoom in by generating a more detailed chart or a new table for the Asia-Pacific region data. All this happens within the same dashboard page, without the manager ever clicking a menu - the interface adapts to their spoken/written requests in real time. The result is a highly interactive, conversational dashboard that is far more intuitive than static BI tools. It’s essentially a LLM-driven product interface tailored to the user’s questions.

Example 3: Onboarding Workflow Assistant - For an HR team, consider an internal tool for onboarding new employees. Rather than a fixed sequence of forms and checklists, a generative UI-powered assistant could guide the HR rep or the new hire through the process with a conversational interface. The AI could start by greeting the user and then generate the first needed form (for personal info). If the new hire indicates they’re transferring from a contractor role, the AI might dynamically insert an extra form section specific to that scenario (something a static form might not have). As documents are uploaded, the AI could display a preview or a checklist that updates in real time. If the user has a question like “What is this policy about?”, the AI can show an excerpt of the policy document in an embedded viewer right then and there. By the end of the chat-based workflow, the user feels like they had a personalized guide through onboarding. The interface wasn’t one monolithic form; it changed based on the conversation. For the HR team, this means less designing different onboarding flows for each case - one generative system handles them all by intelligently altering the UI.

Across these examples (and many others), a few patterns emerge. Generative UI empowers internal tools to mix modalities (text, graphics, forms, etc.) fluidly, driven by an AI that understands the context. Users interact in a more natural way - often via conversation or simple commands - and the tool presents whatever interface is needed to fulfill the request. It’s a fundamentally different philosophy of UI/UX. Instead of the user always adapting to the software (learning where to click, which form to fill when), the software adapts to the user. For enterprises, this can unlock productivity and reduce training needs, since employees can essentially “ask” the tool for what they need and get a tailored interface in response.

Conclusion: Generative UI and the Future of Internal Tools

Generative UI is poised to redefine how internal software is built and experienced across the enterprise. By allowing AI to directly generate and modify user interfaces, we enable a level of agility and personalization in internal tools that was previously unattainable. Frontend design and development, long a bottleneck for deploying new enterprise solutions, can be accelerated and improved through LLM-driven interfaces. As we have seen, a real-time adaptive UI can make an AI-powered tool dramatically more useful and user-friendly - whether it’s by visualizing data automatically, guiding users through complex processes, or tailoring workflows to each individual’s context.

For developers and enterprise tech teams, embracing generative UI will mean rethinking some traditional practices. It introduces a new collaboration between humans and AI in creating software - the AI becomes a sort of junior developer that can assemble UIs under your guidance. This shift can pay off in faster delivery of internal applications, more flexible software that keeps up with changing business needs, and ultimately a better ROI on your AI initiatives (since a great UI lets that AI actually get used effectively).

Thesys is one of the companies at the forefront of this generative UI revolution, building the AI frontend infrastructure to make it a reality. Thesys’s flagship product, C1 by Thesys, is a Generative UI API designed to help developers turn LLM outputs into live, interactive interfaces seamlessly. With C1 by Thesys, teams can let their AI models generate responsive dashboards, forms, and other UI elements in real time - all within the guardrails and style guidelines they define. It’s an approach that promises to cut down frontend development effort while delivering richer user experiences. In fact, C1 by Thesys is already enabling startups and enterprises to build UI with AI, allowing AI tools to generate complete interfaces from simple prompts or responses. If you’re looking to bring the power of generative UI to your internal tools, it’s worth exploring how Thesys can help. The era of static screens is ending; with generative UI and platforms like C1 by Thesys, even internal enterprise software can become as dynamic and intelligent as the AI running behind the scenes. To learn more, you can visit Thesys or check out the documentation and examples of C1 by Thesys on the Thesys docs site. Generative UI is here - and it’s transforming internal tools into adaptive, AI-native experiences that truly unlock the potential of enterprise AI.

(Visit thesys.dev or the Thesys Documentation to learn more about how generative UIs can be implemented in your organization.)

References

Bhandaram, Vishnupriya. (2025, January 28). Internal Tool Builder 101: Everything You Need to Know. Appsmith Blog.
Boston Consulting Group (BCG). AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value. Press Release, 24 Oct. 2024.
Business Wire. (2025, April 18). Thesys Introduces C1 to Launch the Era of Generative UI [Press release].
Deshmukh, Parikshit. (2025, June 10). Bridging the Gap Between AI and UI: The Case for Generative Frontends. Thesys Blog.
Firestorm Consulting. "Stop Patching, Start Building: Tech’s Future Runs on LLMs" Firestorm Consulting
Deshmukh, Parikshit. (2025, June 11). AI-Native Frontends: What Web Developers Must Know About Generative UI. Thesys Blog.
Krill, Paul. (2025, April 25). Thesys introduces generative UI API for building AI apps. InfoWorld.
Firestorm Consulting. "Rise of AI Agents" Firestorm Consulting

FAQ

What is Generative UI?

Answer: Generative UI (GenUI) is an approach where user interfaces are created dynamically by AI (usually using LLMs) rather than pre-designed by humans. In a generative UI, the software’s frontend can change or generate new components on the fly in response to the user’s input or context. This means the UI adapts in real time - for example, an AI might generate a chart, form, or button as needed to best present information or gather input. Generative UIs are AI-driven and context-aware, making the user experience more interactive and personalized than a traditional static interface.

How can generative UI improve internal enterprise tools?

Answer: Internal tools in enterprises often suffer from one-size-fits-all interfaces and slow update cycles. Generative UI can make these tools far more flexible and efficient. For instance, an internal dashboard with generative UI can tailor itself to each employee’s needs - showing relevant data visualizations or input forms based on what the user is doing. This improves productivity because employees aren’t stuck with irrelevant fields or navigating multiple screens; the tool presents exactly what they need when they need it. It also reduces the development burden: instead of constantly coding new UI views for new requirements, the AI can generate interfaces for new tasks on demand. In short, generative UI makes internal tools more adaptive, user-friendly, and quicker to evolve as business needs change.

How do LLMs generate a UI from a prompt?

Answer: Large language models generate UIs by outputting structured instructions that correspond to UI components, a bit like writing a recipe for the interface. When an LLM is integrated with a generative UI system, it’s given knowledge of available UI components (for example, charts, tables, text inputs) and a format to use. If you ask the LLM a question or give it a prompt, it can decide that instead of answering with just text, it should respond with a UI layout. It might output a JSON object or use a special syntax representing a component (e.g., a table with certain data, or a form with specific fields). The generative UI framework (such as C1 by Thesys) then reads that output and renders the actual interface in the application. Essentially, the LLM is figuring out which UI element would best serve the request and describing it, and the front-end code builds it. This is how an LLM can, say, turn a prompt like “Show our sales by region” into a real chart on the screen - the LLM’s answer includes the chart component and data, not just a text explanation.

What are LLM UI components?

Answer: LLM UI components are the pre-built user interface elements that a language model can use when generating a UI. Think of them as a toolkit of widgets that the AI can choose from. Common examples include things like charts, graphs, data tables, forms, buttons, images, or any custom components relevant to the app (for instance, a “user profile card” component). Developers define these components in advance and map them to certain keywords or structures that the LLM will produce. When the model “decides” to show something - for example, a chart - it doesn’t literally draw the chart itself. It will output a reference to the “Chart” component along with the necessary data (such as the dataset or title). The front-end then instantiates that chart component with the data. In short, LLM UI components are the building blocks of generative UIs, allowing an AI to construct an interface by assembling these blocks through its output.

What is C1 by Thesys?

Answer: C1 by Thesys is a Generative UI API offered by Thesys, a company focused on AI-driven frontends. In simple terms, C1 by Thesys is a platform that lets developers feed LLMs (like GPT-4, etc.) and get back live user interface elements as the response. Instead of an AI just returning text, C1 can return interactive components - buttons, forms, charts, and more - that your application can directly render. It’s been called the world’s first Generative UI API, designed to turn LLM outputs into dynamic interfaces in real time. For developers, using C1 by Thesys means you can build AI-powered applications where much of the UI is created on the fly by the AI, while C1 by Thesys handles the heavy lifting of translating that into actual frontend code. Thesys provides documentation and tools (like their Crayon framework for React) to integrate C1 into web applications easily. Essentially, C1 by Thesys is an enabler of generative UI: it gives your AI the “power to paint on the screen,” so to speak, within safe and structured boundaries. This can drastically speed up building internal tools or any AI-driven app, because you no longer have to code every dialog or dashboard - the AI, via C1 by Thesys, will generate parts of the UI for you.

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