UiPath on-prem deployment with Bielik LLM integration - a practical perspective
Everywhere we hear about how RPA is changing business, how digital transformation is the future, and how AI is the answer to everything. But here’s the thing: after several years of running automation projects, I can say one thing for certain - there is a huge gap between how brilliantly something works in a demo and how it actually performs in a client’s production environment.
Over the past few years, I have led dozens of UiPath deployments for various clients - from small companies to large corporations. We recently completed an interesting project involving integration with the Polish Bielik model. And I can honestly say - every such deployment is a serious piece of work. Not because the technology is difficult (although it isn’t simple either), but because the same lesson keeps coming up.
What matters most is not how well you write a bot or how advanced a technology you choose. What matters most is that someone maintains overall control, watches deadlines, and ensures that everyone - on the client’s side and on ours - knows what they are doing and why.
And this is exactly where I see how critical PMO is. Clients who have a solid PMO function operate on an entirely different level than those where everyone does their own thing. With the former, the project runs like clockwork. With the latter… well, sometimes I wonder how we manage to deliver anything at all.
🎯 Why is PMO critical in RPA+AI projects?
Projects combining robotic process automation with artificial intelligence are not ordinary IT deployments. They are hybrid undertakings that require synchronising different technologies, engaging cross-functional teams, and developing new competencies and governance processes. Without a strong PMO function, such projects quickly turn into a technological Tower of Babel, where every team speaks its own language and integration becomes a nightmare.
Our experience shows that PMO in such projects acts not only as a coordinator but also as an architect of trust between all parties involved. PMO builds communication bridges between business and IT, between security teams and developers, between AI enthusiasts and pragmatists focused on business processes.
🔍 Stage 1: POC - A Foundation, Not Just a Proof of Concept
Every automation project starts in a similar way: we identify a process, build a rapid prototype, and demonstrate positive results. In our case, we chose a document-handling process that was ideally suited to combining UiPath’s capabilities with Bielik’s language intelligence. At first glance it looked standard - the robot retrieves documents, extracts data, and passes it on for further processing.
But even at the POC stage, PMO ensured elements that later proved critical. We established clear success criteria that went far beyond a simple “the robot works”. We defined specific metrics: document processing time, accuracy of extracted data, degree of process automation, and also indicators related to the quality of responses generated by Bielik. This was fundamental, because without these metrics we would not have been able to assess whether we were heading in the right direction during later scaling.
In parallel, we began maintaining a risk register, focusing in particular on aspects related to integration with the Bielik LLM. GDPR compliance issues around AI-based data processing, the stability of the language model’s responses, and the security of sensitive data - all of this required careful thought already at the prototype stage. PMO also ensured cross-functional communication, engaging process owners, IT, security, and compliance teams from the outset.
The key lesson from this stage is simple: a POC is not just a technical test - it is the first step in building trust and governance for the entire automation programme. Without solid organisational foundations, even the best prototype will remain nothing more than a technical curiosity.
🏗️ Stage 2: Architecture - The Decision on an On-Premise Environment
Following positive POC results, we faced a fundamental decision: cloud or on-premise? We chose a local solution due to the specific nature of the data we work with. This was not a decision taken lightly - cloud solutions offer faster deployment and lower upfront costs, but in our case the requirements around data control tipped the balance.
Our target architecture included UiPath Orchestrator as the central hub for managing robots, UiPath Robots as the executors of automation processes, UiPath AI Center as the platform for deploying AI models, and Bielik - a Polish language model running locally. This configuration gave us full control, but came with additional integration challenges.
PMO had to coordinate the construction of a context-aware API proxy, which acts as an intermediary layer between the robots and Bielik. This solution controls the flow of requests through throttling mechanisms, logs all interactions for audit purposes, and manages conversation context with the LLM. We also developed input sanitisation logic that removes sensitive data before it is passed to the LLM, standardises prompt formats, and ensures consistent input data quality.
At the same time, PMO coordinated compliance and audit matters, which included creating access policies for AI systems, documenting data flows, and defining compliance procedures aligned with industry requirements. This organisational work proved just as important as the technical development - without it, the entire project would have got stuck in endless approval procedures.
⚙️ Stage 3: The Deployment Model - Governance and Standards
The difference between an experiment and a scalable platform lies in governance. This is the point at which the energy of enthusiasm must be channelled into structured processes and standards. PMO developed a complete implementation framework, which became the foundation for all subsequent automation projects.
We created an automation decision matrix that established clear rules defining what we fully automate, what we delegate to the LLM, and what remains in human hands. Structured, repetitive processes are fully automated, text analysis and response generation are handed over to Bielik, while strategic decisions and unusual cases remain within the competence of employees. This systematisation eliminates the decision-making chaos that often paralyses automation projects.
Documentation standards proved equally critical. We developed robot documentation templates ensuring a uniform format for process descriptions, AI interaction documentation describing prompts, expected responses, and fallback scenarios, as well as testing procedures covering functional tests, prompt regression tests, and security tests. These standards are not bureaucratic overhead - they are tools enabling effective collaboration between teams and rapid onboarding of new members to projects.
PMO also established regular decision points involving DevOps teams responsible for deployment, monitoring and maintenance, IT Security teams handling security, access control and encryption, and AI Governance teams overseeing model quality and ethical AI use. These meetings are not a formality - they are a forum where different perspectives converge to shape final project decisions.
The schedule of checkpoints includes a review after each development stage, an architecture review before deployment, and a post-implementation review with lessons learned. This rhythm gives all stakeholders a sense of control and predictability, which is crucial in projects using such rapidly evolving technologies as AI.
🚀 Stage 4: Production and Scaling - Where Theory Meets Reality
The first production deployments are the moment of truth. This is where the questions arise that every experienced PMO anticipates, but which always sound dramatic coming from business owners: “Why does the robot sometimes freeze?”, “What is our SLA for LLM responses?”, “Why does the AI give different answers than yesterday?”. These questions are not a sign of failure - they are the natural consequence of moving from a controlled test environment into the unpredictable reality of production.
Without an engaged PMO, there would be no consistent system for reporting bugs and tracking regressions, communication between business owners and RPA/AI teams would be chaotic, no systematic register of lessons learned would emerge, and above all there would be no proactive approach to optimisation. PMO provides the organisational stability that allows the technology to operate reliably.
With PMO at the centre of coordination, we successfully implemented incident management tailored to the specifics of a hybrid RPA+AI environment, performance monitoring that links technical dashboards with business KPIs, controlled rollout of improvements without disrupting live processes, and a central knowledge base for future projects. All of this forms an ecosystem that not only responds to problems but anticipates and prevents them.
📈 Results and Business Value
Twelve months after the first robot went live in production, we can share concrete results that best illustrate the value of the whole undertaking. A 75% reduction in processing time means that tasks that previously took employees hours now take minutes. Processing accuracy of 95% or higher shows that combining RPA precision with Bielik’s intelligence delivers better results than either technology alone. Automating 80% of routine tasks has freed employees to focus on work requiring creativity and strategic thinking.
But equally important are the organisational benefits. We built a scalable platform that provides a framework ready for deploying further processes. We improved collaboration between teams thanks to clear roles and responsibilities. Above all, however, we built competencies - we now have a team with real experience in managing projects combining RPA with AI, which is an invaluable asset in today’s market.
🎓 Key Takeaways and Recommendations
Having gone through the entire journey from POC to production, we can identify several fundamental truths worth sharing with other organisations facing similar challenges.
PMO in projects combining RPA with AI is not a progress controller - it is an architect of trust. Its role is to build stakeholder confidence that automation is predictable, controllable, and delivers real benefits. Without this trust, even the best technologies will remain underused.
Governance must precede technology. The best code in the world cannot replace well-thought-out management processes. The governance framework must be ready before the first line of code is written, because attempts to build it after the fact lead to chaos and frustration for everyone involved.
Integrating UiPath with a local model such as Bielik requires more effort than cloud-based solutions, but delivers full control over data and processes. This is an investment that pays off in the long term, particularly in industries with high data security requirements.
Most importantly, however, one must understand that automation is a process, not a project. Success does not end when a robot goes live - it is the beginning of a process of continuous improvement that requires ongoing PMO oversight. Organisations that treat automation as a one-off undertaking are condemning themselves to stagnation in a rapidly changing technological world.
🔮 What’s Next? The Future of Automation atSNOK
Our experience with the UiPath + Bielik deployment is only the beginning of a larger transformation. We plan to expand the platform to cover further business processes already queued for automation. We will also continue to explore new possibilities offered by Polish language models, which are developing at an extraordinary pace. On the horizon, we also see hyperautomation - integration with additional low-code/no-code tools that further democratise automation capabilities. We also plan to implement advanced analytics for deeper analysis of data from automated processes.
🤝 Let’s Build the Future of Automation Together
At SNOK, we believe that sharing knowledge is key to the development of the entire industry. Our experience with the on-premise UiPath + Bielik deployment shows that Polish companies can be pioneers in combining RPA with local AI models. This is not just a technological matter - it is proof that well-managed projects can turn visions into working reality.
Do you have questions about our approach? Are you planning a similar project? Would you like to exchange experiences? Get in touch - we would be happy to share the details. Comment on this post - every discussion enriches the wider community. Share it if you think it could be useful to your network.