Artificial intelligence for technical documentation
Use AI along your content supply chain
We develop AI-powered solutions for technical documentation, including chatbots, self-service applications, and automated content processes. We analyze and optimize your content workflows, model and structure your product and content data, and integrate AI capabilities into your existing authoring, content management, and IT systems.
This enables you to create and update technical documentation more efficiently, maintain consistency across all channels, and provide customers with the right information when and where they need it.
How do successful companies use artificial intelligence in technical documentation?
AI has enormous potential for industry and business. AI is transforming organizations and business processes by streamlining operations, increasing efficiency, improving productivity, and creating new business opportunities.
Technical documentation is no exception. AI is becoming an integral part of the entire content supply chain, from creation and assembly to delivery and interaction with users. AI can generate and translate text, create and optimize images, and answer user questions through chatbots. It can also deliver personalized information tailored to specific audiences. AI agents can analyze change requests, suggest updates to documentation, and process large volumes of content to identify relevant information and patterns.
Your path to AI-powered technical documentation
We identify the most promising AI use cases in your content processes and support the implementation of AI solutions tailored to your business needs. If you want to leverage large language models, such as ChatGPT, deploy AI agents in technical communication, or enhance customer-facing channels with intelligent services, we can help you effectively and responsibly integrate AI into your content ecosystem. This is how we work.
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Artificial intelligence in technical documentation
Using artificial intelligence for technical documentation. This is how we work
- Analyze use cases: We analyze your use cases and requirements for using AI along the content supply chain.
- Review data and documents. We review your data and documentation, identify quality issues and gaps, and prepare your content so it can be effectively used by AI applications and AI agents.
- Verify AI integration. We test the integration with systems for creating and delivering technical documentation and product information.
Learn more about artificial intelligence for technical documentation in our FAQs.
FAQs – Frequently asked questions about AI for technical writing
FAQs – Frequently asked questions about AI for technical writing
Where is artificial intelligence (AI) used in technical documentation?
AI is transforming the way technical documentation is created, managed, and delivered. Typical application areas include:
- Automated content generation, including summaries, descriptions, and complete documentation modules
- Content adaptation for different target audiences, languages, and communication styles
- Automated translation, terminology management, and language quality checks
- Intelligent assistance systems and chatbots that answer user questions directly in web portals and self-service environments
- Automated tagging and metadata generation for iiRDS-compliant information delivery
- Creation and optimization of images, graphics, and illustrations
- Analysis of user behavior and information needs to deliver relevant, personalized content
- Support for editorial workflows, such as assessing the impact of product changes on documentation or analyzing content quality
- Integration of AI into component content management systems (CCMS) to improve quality assurance, consistency, and process automation
How can we ensure the accuracy of AI-generated content?
Although AI supports content creation, it does not replace expert review. We use human-in-the-loop workflows that combine the efficiency of AI with the expertise of technical communicators.
AI generates content suggestions and draft texts, which technical communicators then review and validate. Automated checks ensure consistency, compliance with terminology, and adherence to style guidelines.
This approach guarantees high-quality content tailored to your industry's specific requirements.
How can we prevent AI agents from publishing unverified or incorrect content?
While AI agents can automate tasks within editorial workflows, robust governance mechanisms are essential to ensure that only verified and approved content is published. Typical safeguards include:
- Strict separation of content generation and publication: AI agents are never allowed to publish content directly. Instead, they create drafts that are reviewed through pull requests in docs-as-code environments or approval workflows in component content management systems (CCMSs). Additional quality gates, such as terminology and language checks, can be applied before approval.
- Explicit handling of uncertainty: AI agents are instructed to highlight uncertainties and make assumptions transparent, enabling reviewers to assess potential risks.
- Full traceability: AI agents reference the source of proposed changes, such as development tickets, requirements, or change requests. This allows reviewers to verify the underlying information.
- Controlled versioning: AI agents are restricted to predefined versions, repositories, or branches to ensure controlled, auditable content updates.
Which is better: a centralized enterprise AI platform or the AI capabilities of my authoring environment or CCMS?
A centralized enterprise AI platform offers significant advantages, including strong governance through centralized prompt management, access control, and compliance mechanisms. It also provides greater scalability, enabling AI agents to interact with multiple enterprise systems and business processes.
On the other hand, the AI capabilities embedded in an authoring environment or CCMS are tightly integrated into the daily work of technical communicators. Examples include generating draft content, revising existing information modules, and improving content quality directly within the authoring environment.
As modern CCMSs increasingly provide MCP (Model Context Protocol) servers that enable interaction with AI agents, a hybrid approach is often the most effective option. Context-specific authoring AI functions can be executed within the CCMS, and centrally managed, agent-based workflows can leverage the CCMS's MCP server as part of a broader enterprise AI architecture.