Guideless Raises €1M to Streamline Software Training

The big picture: Lithuanian AI startup Guideless has raised €1 million in pre-seed funding to accelerate its technology development and international expansion. The company offers an AI-powered platform that converts captured workflows into editable, AI-narrated training guides, helping organizations create and update training content more efficiently. Guideless was incorporated in June 2026 and soft-launched its product in late 2025.

Why it matters:

• Combatting outdated documentation: Companies frequently struggle with the time and resources required to create and maintain up-to-date software training materials as teams, tools, and workflows evolve.
• Reducing reliance on support: Outdated or inaccessible documentation often forces employees to seek information from colleagues or support teams, leading to inefficiencies and increased operational costs.
• Preserving institutional knowledge: Guideless aims to build an evolving memory layer that captures and preserves institutional knowledge, ensuring operational context remains current despite changes in personnel or processes.

How it works:

• Automated workflow capture: The platform captures user workflows directly as they are performed within business software, recording each step.
• AI-driven content generation: AI then transforms these individual steps into structured training materials, including visual sequences, written instructions, transcripts, and AI-narrated audio.
• Dynamic and multilingual content: Because workflows are captured step-by-step, tutorials can be easily updated, adapted, and translated. The platform supports text-to-speech in over 40 languages, significantly reducing documentation creation and update times.

The catch: The market for AI-powered documentation and knowledge management tools is becoming increasingly competitive, with many established players and new startups vying for enterprise adoption. Guideless will need to demonstrate clear differentiation and superior ROI to stand out, especially as generic large language models (LLMs) become more capable of assisting with content generation. Maintaining accuracy and relevance across a rapidly changing software landscape will also be a continuous challenge.