50% Reduction in manual validation cycles| Nexus
Project Type
Intelligent automation and AI-driven R&D for BPM-centric data pipelines
Industry
Business Process Management Saas
Tech Stack
ABOUT THE CLIENT
Nexus is a global titan in the AI-powered BPM space, redefining how organizations map, analyze, and optimize their operational DNA. By fusing elite methodologies like Lean and Six Sigma with cutting-edge cloud AI, Nexus empowers enterprises to transform abstract workflows into high-performance engines of efficiency.
OVERVIEW
Our collaboration provided the high-level engineering depth necessary to accelerate Nexus’s AI roadmap. We navigated the complexities of integrating experimental AI with production-grade stability, focusing on R&D for next-generation features.
By designing SQL-based intelligent agents and robust benchmarking frameworks, GeekyAnts delivered a sustainable engineering uplift that paved the way for Nexus’s AI-driven future.
Reduction in manual validation cycles
Faster internal testing workflows
BUSINESS
REQUIREMENT
Nexus sought to push the boundaries of BPM by integrating advanced AI capabilities into its core platform. They required a specialized engineering partner to validate new automation concepts and ensure that experimental R&D could scale into reliable, production-ready features.
Key Requirements:
The business goals included:
1. Executing the AI roadmap for BPM process automation.
2. Implementing and validating various RAG models within existing workflows.
3. Scaling engineering bandwidth to support future-ready R&D experimentation.
SOLUTION
GeekyAnts proposed a modular, scalable engineering framework designed to bridge the gap between AI research and functional BPM tooling.
1. Intelligent Agent Development: Building benchmarkable SQL Agents using POCs across different GPT/RAG models.
2. Automated Validation: Implementing rigorous benchmarking frameworks to ensure AI reliability.
3. Experimental R&D: Prototyping "Video-to-Map" intelligence to convert visual data into BPMN-compliant process maps.
CHALLENGES
IN EXECUTION
& SOLUTIONS
The primary hurdle, Model Selection & Integration, involved finding the right GPT model for BPM-specific tasks. We navigated this by conducting POCs on different RAG models, implementing them as specialized agents to balance accuracy and speed.
To manage Experimental Stability, we balanced R&D with enterprise-grade requirements by creating structured pipelines and validating all features before production.
To address Bandwidth & Expertise constraints without slowing the internal roadmap, our scalable team of AI engineers integrated seamlessly with Nexus to accelerate their 2025 goals. Finally, we ensured smooth Workflow Integration so intelligent insights didn't disrupt the user experience, developing modular components that blended AI-driven analytics directly into existing BPM-centric data pipelines.
Model Selection & Integration
1
Experimental Stability
2
Bandwidth & Expertise
3
Workflow Integration
4
OUR APPROACH
We followed a structured, five-step technical journey to ensure all AI modules were both innovative and industrially sound.
- Step 1: Requirements Alignment
- Step 2: Modular Development
- Step 3: Benchmarking & Automation
- Step 4: R&D Innovation
- Step 5: Review & Knowledge Handover
Requirements Alignment
We initiated the project by defining granular performance expectations with Nexus’s product teams. This involved identifying specific BPM workflows where AI-driven RAG models could provide the highest immediate value.
Modular Development
Our engineers built high-quality SQL Agent logic, ensuring the components were modular enough to be plugged into Nexus's existing platform architecture. We focused on creating clean interfaces for GPT-model integration.
Benchmarking & Automation
To ensure the AI wasn't just smart but also reliable, we automated performance measurements. This allowed us to repeatedly test different RAG models against standardized BPM data sets.
R&D Innovation
We explored the "Video-to-Map" proof-of-concept, pushing the limits of how visual data can be translated into structured process maps. This stage focused on high-risk, high-reward AI experimentation.
Review & Knowledge Handover
The final phase ensured sustainability. We delivered comprehensive documentation and test suites so the Nexus internal team could continue to build upon the frameworks we established.
PROJECT
RESULTS
The collaboration delivered a measurable engineering uplift, changing experimental AI concepts into sustainable assets. GeekyAnts strengthened Nexus’s development capacity, providing the technical foundation for their next generation of operational excellence tools.
Reduction in manual validation cycles
Faster internal testing workflows
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