Katara AI Raises a combined $2.6M USD to pioneer agentic DevX workflows. Read more
AI can save our over 8,700 developer hours annually by automating routine tasks like technical document generation. If you need new docs, you can take advantage of AI agents that learn from your website, codebase, docusaurus, gitbook, discord, telegram, ingest all the information and create beautiful tech docs that are perfectly categorizes.
AI has saved technical writers 9 months of tedious work by drafting rewrites and updates for hundreds of existing tech docs. With a single click writers can publish the updated drafts, maintaining full control over the documentation.
Better tech documentation ultimately means more development and fewer support issues so your project can skyrocket grow without limitation
Katara is an advanced AI-driven platform designed to streamline the categorization, rewriting, and maintenance of technical documentation. By intelligently parsing and analyzing your existing documentation set, Katara can transform messy or outdated files into consistent, well-structured resources. The result is a cohesive knowledge base that’s easier to maintain, navigate, and evolve—without losing critical content.
• Granular Categorization: Katara identifies whether a document is an explanation, a how-to guide, a reference manual, or another type of technical resource.
• Confidence Scoring & Thresholds: The system applies confidence scores to each classification, letting you decide whether a document should be rewritten, improved, split into multiple categories, or left as is.
• Multi-Category Handling: When a document spans multiple categories (e.g., 70% explanation, 30% how-to), Katara can automatically generate separate documents for each category—ensuring no critical information is lost.
• Improvement vs. Full Rewrite: For documents that closely match a target category, Katara applies “improvements” to clean up language and format. For those that are less aligned, Katara performs a more comprehensive “full rewrite.”
• Retention of Essential Details: Even with extensive rewrites, Katara preserves code snippets, tables, and technical references. Users have control over how those elements are reorganized or extracted.
• Multiple Model Support: Katara can leverage several language models—such as OpenAI’s GPT series or Anthropic’s Claude—to generate high-quality rewrites, offering flexibility in style and accuracy.
• Residual Data Capture: Any material not included in the primary rewrite—like reference tables, rare code examples, or command-line parameters—gets placed into a “leftovers” file for review.
• No Information Overlooked: This method ensures that even the most obscure reference material is retained and can be reinserted or filed under a new document category later.
• Large Documentation Sets: Designed to handle hundreds (or thousands) of documents in a single project. For instance, Katara can re-categorize and rewrite 200+ files in a batch.
• Configurable Thresholds: Set custom confidence thresholds so that documents are only fully rewritten or split if they significantly benefit from reorganization.
• Spreadsheet or UI Integration: Katara can export classification results and rewrite recommendations in a user-friendly spreadsheet or a dedicated interface. Users can easily override the system’s suggestions before finalizing new versions.
• Markdown-Focused: Katara creates neatly organized Markdown files—ideal for static documentation sites, Git-based workflows, and continuous integration/continuous delivery (CI/CD) pipelines.
• Flexible Naming & Linking: The platform can rename, move, and restructure files in a consistent hierarchy, but also keeps track of references so you can update internal links.
• Leftover Content Batching: When reference sections or code blocks are removed from the main file, they’re placed in their own “leftovers” folder. You can decide to merge them later, create separate reference docs, or discard them if they’re truly outdated.
• Katara scans each file and determines its primary category (explanation, how-to, reference, etc.) using advanced AI classification techniques.
• It assigns a confidence score to each category, informing you whether the document should be improved, fully rewritten, or split.
• You review Katara’s classification output in a dashboard or spreadsheet.
• Where a document is borderline across multiple categories, you can choose to split that document or override the system’s suggestion to keep all relevant content intact.
• If a document is close to being a perfect match for a category (e.g., 85–90% confidence), Katara uses a lighter “improvement” prompt. This cleans up style, corrects minor errors, and standardizes formatting.
• For documents that require significant transformation, a “full rewrite” prompt is used to restructure the text, ensuring it meets the target category’s style and technical depth.
• Katara recognizes code snippets, CLI references, and other critical details. It preserves these sections during improvements or rewrites.
• If Katara determines certain parts of the doc are purely reference material, it can either keep them in the rewritten file or place them in a separate “reference” file—based on your settings.
• When a document spans multiple major categories (e.g., 70% explanation, 30% how-to, plus some reference content), Katara automatically generates multiple outputs.
• Any unassigned or extraneous text (leftovers) is consolidated into a separate file so you can re-incorporate or re-categorize it manually.
• Katara provides you with a folder of Markdown files, organized by category.
• It can also generate a summary report or spreadsheet detailing each file’s classification, rewrite method, and leftover content.
• You can then commit these files to your Git repository, update internal links, and finalize your new doc structure.
Companies with 200+ unstructured or outdated technical documents wanting a fresh, consistent doc set.
Teams switching from one documentation platform to another (e.g., from a proprietary CMS to Markdown-based docs).
Organizations needing a thorough revamp of existing documentation for new product releases or major updates.
Tech writers wanting to apply consistent style guides, categories, and structures across wide-ranging topics.
Eliminates the need for manual classification and rewriting. Reduces repetitive editing tasks, freeing teams to focus on higher-level strategic docs.
Automatically detects and safeguards vital reference material, code snippets, and commands.
Applies uniform language, formatting, and structure across all documents, making it easier for readers to locate information.
You maintain ultimate control, with the ability to override or adjust Katara’s classification and rewriting prompts.
Works with both small doc sets and enterprise-scale knowledge bases, with flexible AI model integration (OpenAI, Anthropic, etc.).
• Discuss project scope (e.g., 280 documents requiring re-categorization and rewriting).
• Define thresholds, categories, and leftover handling rules.
• Connect AI agents to your systems (e.g. Docusaurus, Gitbook)
• Provide sample documents or entire doc set for Katara’s initial analysis.
• Katara classifies documents, flags multi-category items, and generates improvement vs. rewrite prompts.
• You approve or override suggestions in a dashboard or spreadsheet.
• Katara finalizes the improved and fully rewritten Markdown files.
• Any extracted or leftover reference content is stored separately for your review.
• You commit the final documents to your Git repository or chosen CMS platform.
• Katara offers ongoing support, additional cleanup, or further classification as your documentation needs evolve.
No. Katara places all omitted or reorganized text into a “leftovers” file so you can reassign or merge it later.
Absolutely. The AI engine detects code snippets, API references, and other technical details, ensuring they remain intact or get properly segregated.
Not at all. Katara’s interface (or spreadsheet-based workflow) and straightforward setup make it accessible to both tech and non-tech users.
Katara can be configured to use a variety of LLMs, including OpenAI’s GPT-4, Deepseek R1, and Anthropic’s Claude, depending on your requirements for style, speed, and accuracy.
Katara can be re-engaged for ongoing documentation needs—automatically classifying and rewriting new docs as they come in. You can also train your own internal models or processes to manage smaller updates.
Katara is the solution for teams facing a large, unwieldy set of technical documents. By automatically categorizing, rewriting, and preserving every piece of essential information, Katara helps you maintain a clear, cohesive, and user-friendly doc environment—while saving hours of manual editorial work.
