Nexus
Project Type
Cloud Cost Optimization & Infrastructure Lifecycle Management
Industry
Business Process Management Saas
Tech Stack
About the Client
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.
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.
OUR 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
Experimental Stability
Bandwidth & Expertise
Workflow Integration
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
Modular Development
Benchmarking & Automation
R&D Innovation
Review & Knowledge Handover
RESULTS
Case Studies.
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