SoftServe Gen AI Industrial Assistant

Provide your team with real-time advice and guidance. Accelerated by NVIDIA AI Blueprints, SoftServe Gen AI Industrial Assistant streamlines the navigation of equipment manuals, speeds up troubleshooting, and simplifies maintenance tasks. The solution ensures confident technical knowledge transfer during onboarding, backups, or change management. Additionally, workers benefit from advanced performance analysis and report building through the "Talk to Data" capability, driven by LLMs like Llama and Mistral on NVIDIA NeMo™ platform.

The complexity and technical demands of equipment in the manufacturing and industrial sectors cause operational inefficiencies. They lead to slow manual navigation, troubleshooting delays, and lengthy maintenance. Addressing these issues is crucial, and it’s one growth path.


In response to these challenges, innovation is the answer. SoftServe Gen AI Industrial Assistant digests and analyzes vast data, providing workers with quick access to equipment manuals and real-time guidance. Using data-driven simulation techniques, the solution offers instant KPIs such as overall equipment effectiveness (OEE) and forecasting. This helps workers monitor equipment health, predict failures, and optimize performance.

Manufacturing is changing fast.
SoftServe Gen AI Industrial Assistant keeps your workers up to speed.
created by softserve, accelerated by Nvidia Blueprints
Better Performance
10%
Improvement in overall equipment effectiveness
Quicker Training
50%
Reduction in onboarding time
Higher
Product Quality
56%
Fewer equipment defects via root cause analysis
Faster Data Access
83%
Decrease in average search time for information

Industrial Assistant at a Glance

SoftServe Gen AI Industrial Assistant uses the NVIDIA Gen AI technology stack and a custom user interface for data visualization and search through a knowledge base.

NVIDIA AI Blueprints allow scalable deployment of Generative AI workflows with ease. This enables SoftServe assistant to handle large volumes of data, providing users real-time insights across industrial environments.

Enhanced processing capabilities and optimized tools shorten the time needed to develop, test, and deploy AI models, speeding up the development cycle and allowing the user to quickly benefit from new features.

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  • Drive cost savings and higher profitability
  • Enhance operational efficiency
  • Ensure adherence to safety standards
  • Improve productivity and product quality

How it works

Generative AI

The retrieval-augmented generation (RAG) capabilities of LLMs built on the NVIDIA NeMo stack allow the assistant to use knowledge from your organization’s proprietary documentation. It has access to both historical and real-time production data.

Natural Language Interface

With its voice interface, the solution makes knowledge acquisition quicker and interaction with the system easier, converting spoken language into text and responding in any language.

Customized Dashboard

Gen AI Industrial Assistant features an enhanced UI for quick access to production knowledge bases. It allows for the integration of digitized manuals and guidelines, which are then presented to workers through the dashboard, offering swift, task-specific data analytics.

Solution Architecture

Integrating advanced technologies and optimized tools streamlines operations and
provides a competitive edge. Comprehensive support and collaboration
opportunities within the NVIDIA ecosystem further enhance its value.

NVIDIA AI Enterprise Software Used

  • NVIDIA NIM microservices
  • NVIDIA NeMo Guardrails
  • NVIDIA NeMo Retriever microservices

Use Cases

Troubleshooting and Maintenance

Troubleshooting and Maintenance

Technicians and maintenance supervisors use real-time guidance for step-by-step troubleshooting and quick resolution of equipment issues. With rapid access to equipment manuals, they can minimize errors and reduce downtime by ensuring accurate fixes.
ManufacturingAutomotiveEnergy

Equipment Handling and Safety

Troubleshooting and Maintenance

Technicians and maintenance supervisors use real-time guidance for step-by-step troubleshooting and quick resolution of equipment issues. With rapid access to equipment manuals, they can minimize errors and reduce downtime by ensuring accurate fixes.
ManufacturingAutomotiveEnergy

Performance Monitoring

Troubleshooting and Maintenance

Technicians and maintenance supervisors use real-time guidance for step-by-step troubleshooting and quick resolution of equipment issues. With rapid access to equipment manuals, they can minimize errors and reduce downtime by ensuring accurate fixes.
ManufacturingAutomotiveEnergy

Training Coordination

Troubleshooting and Maintenance

Technicians and maintenance supervisors use real-time guidance for step-by-step troubleshooting and quick resolution of equipment issues. With rapid access to equipment manuals, they can minimize errors and reduce downtime by ensuring accurate fixes.
ManufacturingAutomotiveEnergy

Troubleshooting and Maintenance

Technicians and maintenance supervisors use real-time guidance for step-by-step troubleshooting and quick resolution of equipment issues. With rapid access to equipment manuals, they can minimize errors and reduce downtime by ensuring accurate fixes.
ManufacturingAutomotiveEnergy

“With more efficient knowledge use through tasks like search, summarization and task performance, complemented with real-time guidance on maintenance needs, the assistant led to 10 per cent reduced maintenance and downtime for Continental, improving cost-efficiency and productivity.”

Financial Times

Our Implementation Cycle

01Assessment & Design

  • Outline use cases and challenges during workshops
  • Collect data on the current production environment
  • Define success criteria and constraints
  • Draft use case workflows and user stories
  • Review data foundation and systems for feasibility
  • Create high-level solution design and project plan
  • Duration: 2-4 weeks

02Pilot Phase

  • Design architecture after evaluating tools, services, and tech
  • Set up app and cloud infrastructure and supporting components
  • Design and develop intuitive UI/UX
  • Connect to data sources for error-related information
  • Develop prompt flows for selected use cases
  • Test features, deploy the assistant, and create performance reports
  • Duration: 2-6 months

03Production Rollout & Improvements

  • Appoint a focus group for testing
  • Onboard and train end users
  • Analyze results and feedback
  • Scope, plan, and implement improvements
  • Conduct full-scale rollout
  • Duration: 4-8 weeks
  • Outline use cases and challenges during workshops
  • Collect data on the current production environment
  • Define success criteria and constraints
  • Draft use case workflows and user stories
  • Review data foundation and systems for feasibility
  • Create high-level solution design and project plan
  • Duration: 2-4 weeks

Let's Talk

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