Understand the business target
Let AI understand core business entities such as customers, contracts, projects, inventory, tasks, organizations, and processes.
Enterprise AI-Native Platform
Enabling AI to move from model capabilities to business understanding, operational decision-making, and execution into a closed loop
Why do enterprises need AI-native platforms?
In the AI era, what enterprises need is a foundation that can understand objects, connect systems, drive collaboration, and drive execution
Connect to existing systems
Connects ERP, OA, BI, data sources, and business applications on a unified semantic foundation.
Driving organizational collaboration
Integrate analysis and judgment with tasks, approvals, order assignments, rewriting, and reviews.
Supporting continuous governance
Meet requirements for permissions, auditing, security perimeters, multi-environment releases, and private deployments.
Core competencies
Built an integrated platform around Enterprise Ontology, application construction, decision analysis, intelligent forecasting, business closed-loop, and unified workbench
Enterprise Ontology Engine
Unify core business objects and their relationships, establish a business semantic system covering metrics, rules, permissions, and actions, enabling AI to understand the real business context of enterprises.
Object modelingCore targets such as customers, contracts, projects, and inventory
Semantic relationshipsUnify the relationship between objects, indicator standards, and business rules
Permission boundariesIncorporate organization, roles, and data permissions into semantic governance
Definition of actionThis allows analysis results to be linked to actionable actions
Build the engine
Integrating natural language generation and visual configuration capabilities, it accelerates the construction of applications, processes, agents, and business workbenches, supporting rapid validation and continuous iteration of business scenarios.
Natural language generationQuickly generate application and process sketches from requirements descriptions
Visualized configurationConfigure pages, forms, permissions, rules, and boards
Agent orchestrationEmbedding agent tasks into business processes
Template reuseAccumulate replicable scene templates and component assets
Decision engine
Supports business indicator query, anomaly identification, cause explanation, and business root cause analysis, transforming data insights into management judgments and traceable operational actions.
Indicator queryProviding a unified query entry for business indicators
Exception identificationAutomatically identify key business fluctuations and risk signals
Root cause analysisExplain the business reasons behind the anomaly
Decision recommendationsGenerate action recommendations tailored to characters and scenarios
Prediction engine
Providing trend forecasting and risk alerts for scenarios such as supply chains, projects, contracts, inventory, and capital, enabling enterprises to identify uncertainties in advance.
Trend forecastForecast key metric changes and business trends
Risk warningWarning in advance about risks such as extensions, payment interruptions, and overdue payments
Scene modelDeveloping predictive models around industry scenarios
Feedback calibrationContinuously optimize prediction effects based on operational results
Closed-loop action
Judgment results are linked to task generation, responsibility assignment, approval circulation, result review, and feedback, forming a closed loop from understanding and judgment to execution.
Task generationAutomatically generate tasks, to-dos, and disposal suggestions
Responsibility allocationAssign responsible persons according to organization, role, and authority
Process flowLinkage approval, document dispatch, notification, and system recall
Review feedbackReturn execution results as platform assets
Enterprise Ontology Engine
Ontology is not a data dictionary, nor just a knowledge graph; it defines how the business world is operated by humans and AI together. Core of Ontology: Expressing business with objects, connecting processes with actions, defining boundaries with security, and ensuring controllability through auditing.
Object
Customers, orders, contracts, products, batches, equipment, suppliers, quality events
Relationship
Order attribution, batch traceability, equipment impact, supplier fulfillment, quality correlation
Action
Create CAPA, trigger re-inspection, freeze batches, adjust production schedules, initiate approvals
Safety
Who can see it, who can amend it, who can enforce it, who must approve, and how to leave a trace
Ontology enables AI to move from "understanding data" to "participating in decision-making"
AI can explain and advise, but truly changing production plans, quality status, or customer commitments must fall into action, permission, and auditing.
Application integration construction
Solidify business experience into application systems: super-agents understand objectives and generate logic, while low-code accumulates pages, processes, permissions, and data models. Low code is not replaced by AI, but rather becomes the layer where AI-generated capabilities enter enterprise production systems.
Business intent
Natural language describes goals, rules, exceptions, approvals, and Kanban requirements
AI VibeCoding
Generate code, orchestrate APIs, validate logic, process data, and automate scripts
Low-code visualization
Configure forms, lists, Kanban, flow nodes, role permissions, and publishing
Applied assets
Accumulate into enterprise applications that can be runn, maintained, audited, and iterative
Faster
Validate prototypes such as quality traceability, supplier risk, and operational inquiries within days.
More stable
Pages, workflows, permissions, logs, and publishing all take on AI generation capabilities.
More controllable
Business can participate in configuration, and IT manages complex logic and system connections.
Multi-agent collaboration/digital employees
What companies need is not an assistant, but a digital workforce organization with roles, permissions, memories, and tools. The value of digital employees: Distilling job experience, business rules, and cross-system actions into long-term enterprise capabilities.
Role collaboration
Define agent collaboration boundaries by position, organization, and responsibilities
Tool access
Connecting business systems, knowledge bases, processes, and external services
Authority governance
Execute permission control by role, object, action, and data scope
Meeting decisions
Accumulate meeting minutes, action items, responsible persons, and track results
Task closed-loop
Generate tasks, assign orders, accept, rewrite, and review feedback
Audit Traces
Record the chain of evidence for calls, approvals, execution, and results
Product features
AI-native application construction
Supports rapid generation of applications, processes, agents, and business pages using natural language and visualization.
Enterprise semantics drive operations
Centered on Ontology, it connects objects, rules, indicators, processes, and actions.
Integrated analysis, prediction, and execution
Integrate real-time analysis, trend forecasting, risk warning, and business handling chains.
Multi-role unified work entry
Establish a unified workbench around managers, business staff, operational roles, and digital employees.
Enterprise-level governance and deployment
Meets requirements for permission systems, security boundaries, call management, audit tracking, and operational governance.
Scenario replication and continuous iteration
Accumulate business targets, process capabilities, and application templates, and accelerate pilot validation and reuse.
Product architecture
Centered on Enterprise Ontology, it integrates existing systems, unstructured knowledge, FDE delivery workbenches, and AI application construction and governance into a single operational platform.
FDE governs systems and knowledge into Ontology, delivering digital employees, agent scenarios, and AI-native applications
Enterprise AI application layer
Quality, equipment, procurement, EHS, IT support
Business insights, quality traceability, supplier alerts
Budget / Project / Contract / SRM / QMS / WMS / SCM
Digital employee management platform
Execution layer engine
Ask about the number engine
Indicator engine
Rules engine
Business action engine
AI-enabled business semantic layer
Cleanse systems, SOPs, quality standards, and historical cases into citable knowledge, providing business context and experiential evidence for Ontology
Target · Relationships · Action · Rules · Authority
Ontology + Page + Process
The tool calls + Audit
ERP / MES / QMS / PLM
WMS / SRM / Data Warehouse / CRM
Documents / Attachments / System Files
SOP / Specification / Online Documentation
System mapping and governance
Knowledge cleansing and storage
Ontology modeling
Application construction and launch
Application scenarios
Supply assurance and delivery coordination
From supply-demand risk warnings to decision-making meetings, and then to closed-loop execution
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Project Execution and Execution Management
Build a closed-loop project fulfillment and execution throughout the entire lifecycle
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Contract performance and payment collection control
Build a closed-loop end-to-end process from contract to payment
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Inventory turnover and capital efficiency optimization
Establish a multi-indicator linkage analysis and optimization mechanism
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Transparent supervision of group operations
Build a unified regulatory system across levels, organizations, and systems
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Welcome to book a demonstration of Batter's enterprise-level AI-native platform
Starting from a high-value scenario, it verifies the business closed-loop capabilities brought by Deneb's enterprise-level AI-native platform