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To modernize asset maintenance operations in a manufacturing setup, NeenOpal implemented an AWS-based system using Amazon Q Business. This solution empowers technicians to query equipment issues conversationally, access IoT and document data, and trigger work orders instantly. By integrating AWS IoT SiteWise, S3, Lambda, and Grafana, the project delivers real-time observability, faster diagnostics, and AI-driven insights accelerating asset uptime and operational efficiency across the plant.
Traditional maintenance systems lacked real-time visibility, intelligent search, and automation, leading to delays, inefficiencies, and a heavy reliance on IT teams for support. The following issues were key bottlenecks:
Maintenance records in S3 and tribal knowledge were unstructured and inaccessible without deep searching, slowing issue resolution during asset breakdowns.
Teams had no centralized view of sensor telemetry from IoT devices, leading to delayed decisions and inefficient root cause analysis.
Operators couldn’t trigger or track work orders instantly, causing lag in repair cycles and impacting production SLAs.
Users weren’t familiar with querying data sources like SiteWise or S3 directly, leading to dependency on IT for insights.
Monitoring across CloudWatch, X-Ray, and logs wasn’t unified, limiting visibility into end-to-end system health and usage metrics.
NeenOpal developed a conversational, production-ready solution by combining Amazon Q, AWS IoT, and serverless APIs for seamless, intelligent maintenance operations:
Amazon Q Business was configured with role-based access, enabling natural queries like “What’s wrong with Pump #3?” or “Show downtime logs from last week.” S3-based document connectors and plugin integrations allowed real-time asset information and repair records to be accessible in Outlook or Slack, streamlining communication across shifts.
A simulated Python-based telemetry generator was set up for AWS IoT SiteWise to simulate real-time data streams. These signals fed into Amazon Q Business for proactive alerting and condition-based queries. Sensor thresholds and trends were monitored for actionable triggers to reduce equipment failure risks.
NeenOpal designed lightweight Lambda functions behind API Gateway to allow conversational triggers to open work orders, escalate issues, and fetch past resolution data. These APIs integrated tightly with Q plugins, creating a fluid command-to-action pipeline without leaving Slack or Teams.
In production, Amazon CloudWatch, AWS X-Ray, and Grafana were configured to monitor Q’s query performance, API latency, and plugin usage. IAM policy audits ensured access was secure and aligned with site-level operational roles. This gave stakeholders complete confidence in performance and uptime.
NeenOpal combines AI expertise with deep AWS integration capabilities. From LLM-based querying to real-time telemetry pipelines, we deliver business impact fast. Our understanding of manufacturing KPIs, edge IoT, and production workflows ensures you get usable, scalable solutions not just proof-of-concepts.
The integration of conversational AI and IoT transformed reactive maintenance into proactive, data-driven operations, delivering the following business gains:
By blending Amazon Q Business with IoT SiteWise and custom APIs, NeenOpal transformed how maintenance teams interact with data. The project demonstrated that conversational AI can go beyond office use enabling critical, production-level insights at manufacturing scale without sacrificing control or security.
Data Analyst
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