Slow dismantling of operations
Operational analysis relies on multiple departments to extract data, making it difficult to quickly break down changes in gross profit, expenses, scrapping, and order structure.
High-end manufacturing
Bringing AI into operations, manufacturing, and supply chain frontlines
Competition among high-end manufacturing enterprises ultimately lies in quality stability, delivery certainty, unit cost, capital efficiency, and risk control. Deneb integrates Enterprise Ontology, RAG, multi-agents, digital employees, and enterprise systems, organizing the long chain between operations and the field into an analyzable, traceable, and executable closed loop.
Industry pain points
Enterprises have widely deployed systems such as ERP, MES, QMS, PLM, WMS, SRM, OA, cost control, and data platforms, but in real operations, data scopes, business objects, and process actions are often not uniformly organized.
The quality evidence chain is fragmented
After customer complaints and quality anomalies occur, records of products, batches, materials, equipment, processes, quality inspection, and transportation are scattered, and evidence chains form slowly.
On-site optimization lags behind
Yield fluctuations, downtime risks, and scrap increases are usually only addressed after the results occur, making systematic analysis of the correlation between process and equipment difficult.
Supply risks are not transparent
There is a lack of a unified risk view between supplier delivery, quality, price, inventory, and customer orders, causing delivery urgency and upgrades to lag behind.
Functional service perspectives vary
Functional services such as systems, contracts, fees, IT, and EHS still heavily rely on manual Q&A and cross-system circulation, with inconsistent standards and difficult process traceability.
Key points for plan construction
Empowering AI to understand business, call systems, and drive action
Unify business semantics
Unify the definitions of objects such as products, customers, orders, factories, production lines, processes, batches, materials, suppliers, contracts, inventory, equipment, process parameters, quality events, cost centers, and tasks.
Obtain verifiable factual data
Real-time or quasi-real-time data output conclusions based on ERP, MES, QMS, PLM, SRM, WMS, PLC, Data Warehouse, OA, Expense Control, and other systems.
Understanding systems and historical experience
Search SOPs, quality standards, system documents, contract terms, fee policies, CAPA, maintenance records, exception reports, and historical cases by permission.
Bring analytics into the execution closed loop
Transform warnings, root cause assumptions, and business conclusions into tasks, work orders, processes, or collaborative meetings, and write the results back to business entities.
Operating within the boundaries of governance
Controllability is ensured through job visibility, action authorization, approval control, audit logs, model usage, quality evaluation, and sandbox mechanisms.
The role of the platform: turning AI into an enterprise that can be operated, audited, and reused
Use unified Ontology, system connectivity, multi-agent capabilities, and low-code capabilities, AI can enter the manufacturing and operation field from the Q&A entry point.
Touchpoint: Enterprise AI digital workbench
For CEOs, plant managers, purchasing managers, and functional teams, it offers AI dashboards, AI data checks, digital employee collaboration, as well as integration with PC, mobile, and IM.
Application layer: AI-native business systems
Covering budgeting, projects, contracts, SRM, OA, cost control, as well as QMS, equipment, processes, WMS, EHS, SCM, and other business systems.
Ontology Engine: Enterprise business semantic layer
Through objects, attributes, relationships, actions, functions, process rules, security, governance, and publishing, AI understands the true semantics of automotive glass business.
AI intelligence and construction
Supports agent decision-making meetings, digital employee management, identity and memory, tool integration, permission governance, model usage, and VibeCoding low-code generation.
Knowledge and system integration
Connects to the RAG unstructured knowledge base, as well as systems such as ERP, MES, QMS, PLM, PLC, WMS, SRM, data warehouse, and CRM.
Cloud Market: Reusing enterprise AI assets
Accumulate Ontology industry template libraries, Skill marketplaces, workflow templates, knowledge packs, API action libraries, Agent templates, application templates, and governance strategy libraries.
Key capabilities for solution implementation
From business understanding to closed-loop action
Enterprise Ontology Construction
Unify products, orders, batches, materials, equipment, processes, quality events, suppliers, contracts, inventory, costs, and tasks into a network of business objects understandable by AI.
Knowledge and data connection
Access SOPs, quality standards, system documentation, contract templates, fee policies, historical complaints, CAPA, maintenance records, and vendor agreements via RAG.
Digital Employees and Multi-Agent Systems
Staff are assigned by role in financial analysis, quality traceability, process engineers, equipment operations and maintenance, procurement documentaries, contract review, IT support, and EHS digital staff.
Low-code and action closed loop
Quickly generate business dashboards, risk lists, traceability links, forms, work orders, approval flows, mobile entry points, and IM collaboration entry points.
Solution implementation path
A unified approach facilitates investment evaluation, asset accumulation, and reuse across more operational and manufacturing scenarios.
01
Build Ontology first
Break down products, customers, orders, projects, cost centers, batches, equipment, process parameters, suppliers, contracts, inventory, and quality events into objects, attributes, relationships, and actions.
02
Next, knowledge
Access SOPs, quality standards, system documents, contract templates, fee policies, historical complaints, CAPA, maintenance records, and vendor agreements with RAG.
03
Access system
Access ERP, MES, QMS, PLM, PLC, WMS, SRM, data warehouse, OA, and cost control through external system mapping, MCP, API action libraries, and connectors.
04
Orchestrating agents
Configure digital staff for financial analysis, quality traceability, process engineers, equipment operations and maintenance, procurement tracking, supply chain collaboration, contract assistants, and IT support according to scenarios.
05
Generate applications
Generate Kanban, forms, workflows, lists, mobile portals, and IM Q&A portals using VibeCoding, low-code, workflow templates, and application templates.
06
Governance and operations
Ensure AI has boundaries with RBAC, visibility control, approvals, audit logs, model usage, quality assessments, runlogs, sandboxing, and enterprise deployments.
High-end manufacturing high-value business scenarios
Focusing on five main chains—operations, quality, manufacturing, supply chain, and functional services—the company organizes analysis, judgment, and execution into a reusable closed-loop.
Business analysis
When gross profit, budget, expenses, project revenue, or cash efficiency deviate, financial, manufacturing, procurement, and order data are often scattered across multiple systems, resulting in long operational analysis cycles and delayed rectification actions.
Construction methods
Establish operations Ontology around organizations, factories, products, customers, orders, projects, budgets, cost centers, suppliers, inventory, yield rates, and other entities, and access ERP, MES, SRM, data warehouses, OA, and cost control data.
Application process
Managers raise questions in business language at the AI workbench, the platform locks in indicator definitions and permission scopes, and multiple agents break down factors such as procurement costs, scrapping, expenses, and order structure.
Solution Effectiveness
- Shorten cross-system data collection and operational analysis cycles
- Increase transparency in indicators such as gross profit, expenses, scrap, and inventory
- Transform business analysis conclusions into traceable items such as procurement negotiations, cost reviews, yield improvement, and inventory clearance
Quality traceability
After customer complaints or quality anomalies occur, quality, manufacturing, engineering, supply chain, and sales teams need to form a complete chain of evidence in a short time, and decentralized systems affect investigation efficiency and conclusion quality.
Construction methods
Establishing a traceability Ontology centered on customers, products, batches, materials, suppliers, equipment, process parameters, quality inspection records, complaint forms, and CAPA, connecting QMS, MES, PLM, WMS, ERP, and other systems.
Application process
Customer complaint forms or QMS anomalies trigger the Quality Assistant, the system automatically links batch history and related records, searches for similar cases and regulatory standards, and organizes multi-role consultations.
Solution Effectiveness
- Accelerate the initial investigation of customer complaints and abnormalities
- Enhance the integrity of traceability chains, evidence citation, and accountability for actions
- Promote CAPA from issue registration to action, verification, response, and review closed loops
Process optimization
Yield, scrapping, line stoppages, and unit costs are influenced by multiple factors such as process parameters, equipment status, material batches, defect types, and team differences, making it difficult to identify these combinations in traditional analysis.
Construction methods
Establish a manufacturing Ontology centered around factories, production lines, processes, equipment, process parameters, material batches, defect types, yields, scrap amounts, teams, and maintenance records, and integrate MES, QMS, PLC, and equipment maintenance data.
Application process
The system continuously monitors yield and scrap indicators, identifies anomalies and correlated variables, and proposes controlled trial recommendations; Key parameter changes enter authorization and approval, implemented after engineer confirmation.
Solution Effectiveness
- Identify the key combination of factors affecting yield and scrap earlier
- Include scrap amounts in operational evaluations for process optimization
- Enable controlled records of parameter adjustments, trial validation, and effect reviews
Supplier alerts
Delays in critical materials, quality fluctuations, or price anomalies can quickly spread to production scheduling, inventory, customer deliveries, and cash occupation, making it difficult to identify risks proactively through manual delivery urging alone.
Construction methods
Establish the supply chain Ontology focusing on suppliers, materials, purchase orders, contract terms, delivery batches, quality anomalies, inventory levels, production scheduling plans, and customer orders, connecting systems such as SRM, SCM, ERP, WMS, and more.
Application process
The platform scans orders, inventory, delivery commitments, and quality anomalies daily, generating risk orders, impact amounts, inventory coverage days, and recommended actions; After authorization, initiate processes such as urging payment, upgrading, alternative evaluation, or transfer.
Solution Effectiveness
- Shift supplier performance management from post-payment reminders to pre-payment warnings
- Uniformly displays the risk relationships between procurement, inventory, production scheduling, and customer delivery
- Reduce additional cost risks from supply disruptions, rushed procurement, and line shutdowns
Digital employees
Functional tasks such as system consultation, contract approval, expense reimbursement, IT work orders, and EHS reminders are frequent and repetitive, with manual Q&A taking up a lot of time and making it difficult to maintain consistent standards over the long term.
Construction methods
Integrate policy documents, SOPs, contract templates, expense policies, IT manuals, and EHS specifications into an authorizable knowledge package, and configure financial assistants, contract assistants, IT support assistants, and EHS digital staff by role.
Application process
Employees raise questions on PC, mobile, enterprise IM, or business halls. The system searches for sources by organization, position, and permission, and after material review, enters the corresponding process.
Solution Effectiveness
- Reduce repetitive system Q&A and manual order transfer
- Solidification of policies, contracts, and expense handling standards
- Establish operational dashboards covering knowledge gaps, service efficiency, and process timeliness
Business value
From local efficiency improvements to measurable intelligent operations
Operating value
Analyzing costs, gross profit, scrapping, inventory, procurement risks, and order structure within the same operational semantics helps management more quickly identify sources of deviations, scope of impact, and responsibilities.
Quality value
Through batch traceability, knowledge citation, historical case comparison, and CAPA closed-loop, shorten the chain of anomaly detection and cross-departmental collaboration.
Creating value
Linking equipment, processes, materials, defects, yield, and costs provides traceability for controlled testing, parameter optimization, maintenance decisions, and on-site reviews.
Supply chain value
Transparency of supplier fulfillment, purchase orders, inventory, production scheduling, and customer delivery relationships, proactively identifying supply interruptions, delays, price, and quality risks.
Organizational values
Distill policies, norms, processes, experiences, and job actions into authorizable knowledge packages, digital employees, and workflow templates.
Management can see the results of the business
Gross profit, costs, budget deviations, cash efficiency, risk exposure, and investment returns.
The factory can close the loop for quality and efficiency
Quality traceability, production anomalies, process parameters, equipment predictive maintenance, and EHS.
Enterprises can control the boundaries of AI
Permissions, approvals, audit logs, model usage, sandboxing, and enterprise deployment.
The supply chain can identify risks in advance
Supplier risk, procurement tracking, inventory levels, contract terms, and delivery alerts.
Building a closed-loop AI-native operation for high-end manufacturing
Starting from high-value scenarios such as business analysis, quality traceability, or supplier alerts, accumulate replicable enterprise AI assets.