Enterprise AI has officially moved beyond theoretical concepts and is proving its worth in pre-production. At the recent Cloudera Partner Hackathon in Australia, four leading partner teams demonstrated how AI can solve real, complex operational problems leveraging the power of the Cloudera platform.  The judging panel faced an outstanding lineup of presentations, assigning ranks to each participant based on the following criteria:  Innovation & Creativity Technical Understanding Business Impact Presentation & Pitch While the quality of all presentations was remarkably high, the judging panel selected Citadel Edge and Atturra as the winners based on their innovation, creativity, and the potential business impact of their solutions. These winners were invited to showcase their use cases at the ADAPT AI roundtable event with AWS held in Sydney on the 7th of May 2026.  This event was  a premier opportunity for the AI Hackathon winners to join an exclusive discussion with senior Data Heads and C-level executives. Below is an in-depth look at the use cases, solution architectures, and business value presented by each of the four exceptional hackathon teams.  Winner: Citadel Edge: Watch the video "What the Cloudera Hackathon reinforced is that organisations aren’t struggling to adopt AI - they’re struggling to operationalise it in a secure, governed way that delivers real business outcomes. Our ‘RFP to PoC builder’ shows how you can bring AI directly to trusted data, automate complex workflows, and still maintain human oversight and traceability. That’s the shift the market is demanding - moving beyond experimentation to production-ready AI that reduces cost, accelerates delivery, and builds confidence in every output.” - Todd Trevillion, Executive General Manager - Strategic Partnerships and Relationships, Citadel Edge. The Solution Architecture Use Case: RFP to PoC Builder A platform designed to ingest RFP and requirements documentation, extract all defined requirements and deliverables, and generate a fully functional proof of concept (PoC) or minimum viable product (MVP) within a chosen technology stack. Solution Architecture: Citadel Edge developed a comprehensive nine-stage pipeline, orchestrated via a custom Python application hosted on Cloudera AI. The solution integrates Cloudera RAG Studio for document ingestion and vectorisation, and Cloudera Agent Studio to enable agent-driven workflow orchestration. RFP evaluation is performed using an “LLM-as-a-Judge” approach, followed by a multi-stage code generation process leveraging Amazon Bedrock and the Claude Code SDK. This process systematically produces database schemas, APIs, user interfaces, and test scripts. The architecture incorporates multiple human-in-the-loop checkpoints to enforce governance, validation, and quality control. End-to-end traceability is maintained throughout, ensuring all outputs are attributable to specific source requirements, with all data securely retained within a private, controlled environment. Business Value:  The RFP-to-PoC Builder significantly accelerates pre-sales and solution development workflows, reducing delivery timelines from several weeks of manual effort to a matter of hours. It removes the burden of processing extensive documentation from senior engineers while preserving complete traceability, enabling every generated artifact to be linked back to original RFP requirements. Over time, the platform builds and compounds organisational intelligence, enabling faster, more consistent, and higher-quality responses with each subsequent engagement. Winner: Atturra: Watch the video “Our winning solution in the Cloudera AI Hackathon showcased how agentic analytics makes self-service BI achievable. ​Our aim was to showcase how agentic analytics brings us much closer to the vision for self-service BI and data exploration and, how this generates greater interest in data analytics amongst business users. ​ What stood out was how quickly we could deliver meaningful, evidence-based insights from complex datasets without requiring SQL or deep data expertise. Cloudera makes this possible in a hybrid architecture, by bringing together the power of LLM’s, agentic analytics and a unified data layer. ​ This is a truly impressive capability to building confidence in data-driven decision-making.”- Petar Bielovich, GM Data & Analytics, Atturra. The Solution Architecture Use Case: Our self-service agentic analytics solution was framed around the ‘Workforce Skills Gap Analyser”  is - a fully private, Agentic AI platform that answers plain-English questions about Australia’s skills shortages. A user types a question, an AI agent decides which data to query, runs multiple queries against a governed data layer, reasons over the combined results, and returns a specific, explainable answer with full source transparency. Solution Architecture: Atturra’s architecture is rooted in the Cloudera platform, transforming open government data into Iceberg "gold" tables mapped within a Cloudera Data Warehouse semantic layer. On top of this, they built an orchestrator agent in Cloudera Agent Studio that routes natural language queries to five distinct agentic analytical tools (e.g., Shortage status, Vacancy trend, Projections, etc). Cloudera RAG Studio is run in parallel to ingest policy contexts, ensuring the agents synthesise both structured data and unstructured rules accurately. Business Value: Atturra tackled the widespread issue of "dashboard debt" where organisations maintain hundreds of dashboards that practically no one uses. By enabling conversational analytics, they empower business leaders to ask complex questions and receive instant, explainable answers. This effortless interaction drastically accelerates the time-to-decision and creates a reinforcing loop that organically drives data quality improvements and data literacy across the enterprise.  Runner-up: Skillfield “The Cloudera Hackathon gave the Skillfield Team a great opportunity to test how AI can move from insight to action in a real software operations scenario. Using Cloudera’s AI services and platform capabilities, including LLM support, RAG Studio, Agent Studio, model registry, and jobs, we were able to build a working AI self-healing platform in a short hackathon timeframe. Cloudera platform helped us bring the pieces together: reading operational signals, classifying errors, adding business and technical context through RAG, generating proposed fixes, and keeping the human in control before anything is shipped. For us, the real value was that Cloudera gave us a practical environment to turn a 3 AM production issue into a reviewable fix that a developer can assess with their morning coffee.”- Mouaz Alnouri, Managing Director, Skillfield Use Case: An AI Self-Healing Software Platform that monitors application logs in real-time, diagnoses the root causes of errors, and auto-generates code fixes for human approval. This transforms reactive support into proactive maintenance using the Cloudera platform. Solution Architecture: Skillfield's solution ingests real-time application logs into Cloudera. It uses Cloudera AI to classify error severities and parse stack traces. Using Cloudera Agent Studio and RAG Studio, the platform analyses the specific codebase in the connected Git repository and generates a proposed code fix. Once a human operator approves the generated fix via the UI, the platform triggers the CI/CD pipeline to seamlessly deploy the code into a staging environment for automated testing. Business Value: Development teams currently lose up to 40% of their time to firefighting and production triage. Skillfield's platform slashes Mean Time To Resolve (MTTR) from hours to seconds. By transforming overnight emergency alerts into routine morning Pull Requests, this self-healing approach frees up critical engineering resources, cuts operational support costs, and safeguards substantial IT investments. Outstanding Entry: Indigitise The solution was developed by the Indigitise Hackathon team called the Data Avengers, including Vamshidher Laxmannagari, Rajan Manchi, and Sathish Kumar Soundrapandian, whose expertise, innovation, and dedication were instrumental in delivering the outcome.  "The Cloudera AI Hackathon demonstrated how organisations can move beyond experimentation and apply AI to solve complex operational challenges at scale. The Indigitise Sovereign Innovation Engine was designed to address a fundamental government challenge: transforming large volumes of unstructured submissions into transparent, evidence-based, and auditable decisions. By combining secure data platforms, advanced AI models, and governance-driven workflows, we showed how agencies can significantly reduce assessment effort, accelerate decision-making, and improve consistency while maintaining accountability. We believe this approach represents a meaningful step toward the next generation of AI-enabled public sector operations."- Rakesh Lakkarasu, Director, Indigitise Pty Ltd Use Case: The Cloudera Sovereign Innovation Engine is an AI-assisted governance platform built for defence and federal agencies, which converts unstructured innovation submissions into ranked, defensible, and auditable recommendations without human intervention in the initial triage phase. Solution Architecture: Indigitise built a Python application launched via the Cloudera AI Workbench. It systematically standardises unstructured submissions (PDFs, presentations, Excel) using an intelligent Natural Language Processing (NLP) scoring engine backed by Anthropic's LLMs. The system routes these documents through a complex rules engine that evaluates them across multiple categories—such as strategic alignment, cost, and security—while maintaining a robust audit log for statistical analysis. Business Value: The Sovereign Innovation Engine targets the massive administrative bottleneck of public procurement and RFI triage. Indigitise estimates their solution delivers a 90% reduction in manual triage time and a 75% faster time-to-shortlist for vendors. For departments processing thousands of submissions over an 18-month cycle, this engine offers an unbiased, scalable, and 100% auditable RFP evaluation process. "As the Cloudera Regional Partner Lead, three key aspects stood out through the competition. First, the speed at which partners turned ideas into working solutions was impressive. Teams focused on solving real business problems rather than spending weeks on infrastructure and integration challenges. Second, I was pleased to see governance embedded from the start. Our partners are not just building AI demos; they are creating solutions with the security, lineage, and control required for enterprise adoption. Finally, the collaboration across the ecosystem was exceptional. The willingness of partners and the Cloudera team to share ideas and support one another created an environment where innovation could thrive. It was a clear example of how Enterprise AI delivers the best outcomes when technology, governance, and people come together.” - Aroon Wadvani, Cloudera, ANZ. The Democratization of Innovation It became apparent during the hackathon week that AI has democratized the barrier for teams to innovate. You need not be a specialist coder or a specialized data science expert to build a prototype. It does help to have specialized skills, and AI can augment those to help individuals and teams innovate faster. Some common themes were seen across the teams: Rapid UI Dev: Application front-end development was done in a short duration by all teams. Ecosystem Fit: Integrating the applications to the Cloudera ecosystem needed some guidance. Agentic Tools: AI usage helped the teams partner with and augment the agentic tools that fit their key requirements. Lean Teams: Small, modular teams of 2–3 people were able to deliver functional and operational systems. Domain Context: Teams worked on key problems they were seeing in their own consultancy space, giving them deeper insight into operational challenges. Because of this, it became imperative that clarity of requirements and success/failure criteria remain very important elements in the AI age. Code Tradeoffs: There were clear considerations by each team on where AI vs. regular operational code is valuable, factoring in margins of error, latency requirements, and key success criteria. Key Success Factors The most successful projects filtered their ideas through a practical framework, answering these core questions before building: Value & Baseline: What specific problem are we solving? What is the baseline? What does success look like? Feasibility: Do we have the data? Is a GenAI/LLM even necessary? What are the latency and availability requirements? Risk & Security: What is the acceptable error rate? What are the data privacy and security constraints? Scale: What is our budget for a hackathon, and how do we expand the prototype in a business context for greater value with end stakeholders? Why Cloudera  Cloudera’s Agentic AI framework and AI Workbench provided the right answers to the above challenges with: Data Management: An integrated hybrid data platform to curate and manage data for AI consumption. Architectural Optionality: Providing traditional machine learning approaches via the AI Workbench versus GenAI capabilities using Cloudera AI studios and MCP services. Private AI Infrastructure: Addressing privacy and security concerns with Cloudera Private AI Inference—a fully secure infrastructure within your environment where you own the model, the data, and the complete interactions. No external vendor or third party has any access to it. No Vendor Lock-In: Complete flexibility to innovate with open-source tools, frameworks, and packages. With the fast pace of AI, it is important to maintain that optionality and control of your AI investment rather than waiting for a vendor to implement it in their roadmap. Ultimately, these teams didn't just build clever prototypes, they laid the groundwork for secure, scalable enterprise transformation. If you have a compelling use case that would benefit customers or are interested in a future Cloudera AI Hackathon in your region, please reach out to your partner team at Cloudera. ... View more