The Client:
A Global Manufacturer of Mission-Critical RF and Microwave Solutions
The client is a global leader in high-performance RF and microwave signal processing and conditioning, and electromagnetic interference protection. For decades, the company has provided specialized equipment for military, energy, federal, and private sectors and environments where precision, reliability, and engineering excellence are non-negotiable.
Company Profile:
- Decades serving defense, energy, and federal sectors with mission-critical RF/microwave equipment
- Complex multi-level Bill of Materials (BOMs) spanning thousands of components
- Meticulous engineering processes requiring deep institutional knowledge
- Manufacturing operations across sourcing, inventory, orders, and production planning
- Engineering teams managing intricate parts relationships and supply chain dependencies
The company's engineering and operations teams relied on manual processes to analyze BOMs, answer "what-if" scenarios, and make sourcing decisions. Experienced engineers held decades of institutional knowledge that wasn't systematically captured, shared, or accessible to new team members.
When engineers needed to analyze inventory levels, forecast lead times, or evaluate alternative parts for complex assemblies, they manually searched spreadsheets, cross-referenced multiple systems, and relied on memory and experience. Questions like "What happens if Supplier X delays Component Y?" or "Which work orders are affected by this parts shortage?" required hours of manual investigation.
info The data existed across multiple systems, but extracting actionable insights demanded significant engineering time and expertise that could have been spent on higher-value work.
The Problem:
Manual Engineering Analysis Slowing Velocity and Hiding Critical Insights
A wealth of manufacturing data trapped across disconnected systems, forcing engineers to spend hours manually analyzing BOMs, inventory, and orders instead of solving strategic problems.
Engineering Efficiency:
- Hours spent manually analyzing data instead of engineering work
- Repetitive queries for the same operational questions
- Limited ability to explore complex "what-if" scenarios
- Dependency on senior engineers for institutional knowledge
Operational Responsiveness:
- Slow identification of supply chain risks and impacts
- Delayed responses to parts shortages or supplier issues
- Manual coordination across engineering, sourcing, and manufacturing
- Limited visibility into downstream effects of upstream changes
Knowledge Management:
- Institutional knowledge lost when experienced engineers departed
- New engineers required months to build context and understanding
- No systematic way to capture engineering tradecraft
- Tribal knowledge created single points of failure
System Integration:
- Aging ERP system with no intuitive query interface
- No cross-functional visibility across BOMs, inventory, and orders
- Engineers needed technical expertise just to extract basic reports
- Data silos prevented holistic analysis of manufacturing operations
JBS Dev's Approach:
AI-Enabled BOM Platform with Natural Language Intelligence
Rather than replace the existing ERP system, JBS Dev built an AI-enabled agentic platform layered on top of the company's existing data infrastructure. The solution uses Azure AI Services, Django REST Framework, and React to provide a conversational interface where engineers ask simple questions and receive instant, accurate analysis.
The platform delivers three core capabilities:
1. Natural Language BOM Querying
Ask complex questions in plain English. No SQL or technical expertise required
2. Multi-Agent Intelligence with Institutional Memory
Specialized AI agents orchestrate workflows, apply business rules, and learn from expert feedback
3. Real-Time Cross-Functional Visibility
Unified view across BOMs, inventory, orders, and sourcing, exportable to Excel or PDF
Engineers can now ask questions like "Which work orders are affected by a delay in Component X from Supplier Y?" and receive immediate answers with supporting data, alternative recommendations, and impact analysis without opening a spreadsheet or writing a database query.
The platform is scalable, domain-aware, and built to preserve engineering tradecraft while accelerating operational decision-making.
Multi-agent AI platform built on existing ERP infrastructure without disruption
The Transformation:
From Manual Spreadsheets to AI-Powered Intelligence
cancel Before: Manual Analysis
check_circle After: AI-Powered Platform
VS HOURS
Complex BOM and inventory analysis in minutes, not hours
Language
PLAIN ENGLISH
No SQL or technical expertise required
Agent
AI ORCHESTRATION
Specialized agents handle complex engineering workflows
How We Built It: AI Platform Development
Foundation
Azure infrastructure, data discovery, and foundational SQL Agent for natural language querying
Intelligence
Domain-specific agents, orchestration logic, and conversational memory for context-aware interactions
Advanced Automation
Self-learning mechanisms, advanced workflows, and end-to-end validation for production readiness
verified_user Human-in-the-Loop AI for Mission-Critical Decisions: Engineers validate AI outputs, correct errors, and teach the system, ensuring accuracy while preserving institutional control over critical manufacturing decisions.
What Made This Work
Multi-Agent Architecture for Complex Engineering Logic
JBS Dev designed specialized AI agents, each responsible for specific domains like BOM analysis, inventory forecasting, or supplier relationships. Agent orchestration handles complex workflows that no single AI model could manage alone.
Built on Existing Infrastructure Without Disruption
The AI platform integrates seamlessly with the legacy ERP system via APIs and data connectors. No "rip-and-replace". Manufacturing operations continued uninterrupted while the new intelligence layer was deployed alongside existing processes.
Domain-Aware AI Tuned for Manufacturing Context
Generic LLMs don't understand BOMs, parts relationships, or manufacturing constraints. JBS Dev tuned the AI with client-specific terminology, business rules, and engineering tradecraft, ensuring responses aligned with operational reality.
Human-in-the-Loop for Trust and Accuracy
Engineers validate AI recommendations, correct errors, and teach the system. This feedback loop preserves institutional control while allowing the AI to learn and improve, building trust in mission-critical manufacturing decisions.
Conversational Memory for Context-Aware Interactions
The platform remembers previous questions, user preferences, and project context across sessions. Engineers don't have to repeat themselves or re-explain background. Conversations flow naturally, accelerating analysis.