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Python RPA team held an RPA Hackathon at Kazakhstan’s Stock Exchange

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We Held an RPA Hackathon at Kazakhstan’s Stock Exchange From February to April, we ran a process automation hackathon for the KASE team — it turned out to be dynamic, valuable, and with great results. KASE employees stepped into the roles of process analysts and developers:• they searched for tasks worth automating;• learned how to structure process descriptions clearly;• and then built their first robots using low-code tools. For many, it was their first experience with RPA — and judging by the feedback, a very inspiring one. What was especially great to see was that even participants without a technical background were able to build simple bots and see the real benefits of automation. Once again, I’m convinced that digital transformation is all about people. It’s not some abstract concept — it’s a very real and practical tool for boosting efficiency. Thank you to everyone who took part — and a special thanks to KASE for being open to new ideas!

AI Meets RPA: Python RPA has Unlocked a new level of Automation with LLM module

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AI Meets RPA: Python RPA has Unlocked a new level of Automation with LLM module In today’s fast-paced digital landscape, intelligent automation is no longer a future vision is a necessity. Traditional RPA has helped businesses eliminate repetitive tasks and reduce manual labor. But with the rise of large language models (LLMs), we’re stepping into a new era of AI-driven automation. Our team is proud to introduce a powerful new feature in Python RPA Studio 1.85: AI Module — a seamless integration of LLMs like ChatGPT, Gemini, Claude, and others, directly into your automation workflows. What this means for RPA Developers Until now, RPA excelled at deterministic, rule-based tasks: data entry, screen scraping, form filling, and so on. But RPA traditionally struggles with tasks that involve unstructured data, natural language understanding, or decision-making under uncertainty. That’s where LLMs come in. With the new LLM module in Python RPA, you can now: – Interpret and extract meaning from emails, PDFs, chats, and reports; – Make context-aware decisions within automated workflows; – Generate natural language responses for customer service and reporting; – Automatically classify, summarize, and translate documents; – Process complex data inputs with human-like reasoning.  Real-world Value of LLM-Powered Automation The benefits of combining LLMs with RPA are already being realized across industries: 80–90% reduction in email triage time by using AI to classify and route incoming messages 60–70% faster processing of legal and financial documents with AI summarization Automated customer responses with human-level tone and context understanding End-to-end process automation in cases previously too complex for RPA alone. Built for Developers. Designed for scalability. Whether you’re automating invoice processing, HR onboarding, or regulatory compliance, the LLM integration is designed to be as flexible as Python itself. Python RPA now enables: Unified workflows combining deterministic bots and generative AI Support for OpenAI, Google, Anthropic, and other providers Custom prompt engineering, parameter tuning, and response parsing With this release, we’ve bridged the gap between rule-based bots and cognitive automation and the possibilities are enormous.