Insufficient industry application
Business is difficult to manage fully online and process-driven, with many steps relying on manual recording and offline collaboration.
Agricultural sector
Bringing AI into livestock, breeding, inspection, and supply chain sites
Covering the entire business chain including livestock, breeding, testing, supply chain, contracts, and equipment management, BetterAid helps agricultural enterprises build a unified industry foundation, supporting business integration, data centralization, and intelligent operations.
Industry pain points
The business of agricultural and livestock enterprises covers multiple stages including suppliers, contracts, testing, breeding, equipment, supply chain, and cost accounting. Traditional informatization often faces issues such as fragmented modules, system fragmentation, and difficulties in scaling old systems, making it hard to meet industry demands and achieve full-link data collaboration.
Legacy system expansion is weak
Old systems are costly to use and have weak scalability, making them unable to support new agricultural business scenarios.
Data silos are severe
Data from suppliers, testing, breeding, equipment, and supply chains are scattered and difficult to analyze in a unified manner.
Process traceability is difficult
The breeding, testing, contract, and equipment record chains are long, resulting in low efficiency in manual traceability.
Operational analysis is lagging behind
Cost, supply, inspection, production, and contract data cannot be promptly consolidated into a unified operational view.
Key points for plan construction
Empowering AI to understand business, call systems, and drive action
Unifying the industry foundation
Supporting core agricultural business with unified identity, organization, permissions, processes, and data models.
Supplier collaboration
Covering supplier access, lifecycle, performance, quotation inquiries, bidding, and contract collaboration.
The detection process is closed-loop
Centralized online sampling collection, task allocation, testing, result entry, review, and report submission.
Breeding production management
Support planning, breeding selection, experiments, breeding, cost accounting, and business analysis.
AI-assisted operations
Enhance the precision of agricultural management through anomaly alerts, trend analysis, knowledge Q&A, and operational insights.
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 moves from the Q&A entry point into the agricultural business field.
Touchpoint: Enterprise AI digital workbench
For supply chain, inspection, breeding, equipment management, and operations management teams, it offers AI Kanban, AI data inquiry, digital employee collaboration, as well as PC, mobile, and IM integration.
Application layer: AI-native business systems
Centered on high-value agricultural scenarios, processes and data, knowledge, and collaboration are accumulated into configurable and reusable business applications.
Ontology Engine: Enterprise business semantic layer
Through objects, attributes, relationships, actions, functions, process rules, security, governance, and publishing, AI understands the true semantics of industry business.
AI intelligence and construction
Supports agent decision-making meetings, digital employee management, identity and memory, tool integration, permission governance, model usage, and low-code generation.
Knowledge and system integration
Connecting RAG's unstructured knowledge base with existing systems such as SRM, LIMS, breeding systems, equipment systems, contract systems, and supply chain systems, it creates a closed loop of business data, institutional knowledge, and process actions.
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
Unified modeling of suppliers, contracts, samples, testing tasks, breeding batches, equipment, costs, and supply chains.
Process Center
Handle processes such as market entry, quotation inquiry, testing, audit, reporting, production planning, and cost accounting.
Rules engine
Supports supplier scoring, testing standards, anomaly alerts, approval rules, and cost accounting rules.
AI insights
Combined with intelligent Q&A, anomaly detection, trend forecasting, and business analysis capabilities.
Solution implementation path
A unified method facilitates investment evaluation, asset accumulation, and reuse across more industry scenarios.
01
Organize full-chain business
Priority construction scenarios focused on supplier recognition, contracts, testing, breeding, equipment, and supply chain identification.
02
Build the agricultural Ontology
Standardize suppliers, samples, batches, tasks, equipment, costs, contracts, and business targets.
03
Connect to existing systems
Integrate R&D platforms, organizational platforms, supply chain systems, contract systems, and inspection systems.
04
Configuration industry applications
Quickly set up SRM, LIMS, breeding production, equipment, and cost analysis applications.
05
Combined with AI capabilities
Introducing Q&A, anomaly alerts, trend analysis, and operational insights.
06
Replicated in more scenarios
Expand suppliers, testing, and breeding templates to more business units.
High-value business scenarios in the agricultural industry
Focusing on the core business chain of the current industry, we organize analysis, judgment, and execution into a reusable closed loop.
SRM supplier collaboration
LIMS inspection management
Breeding production management
Equipment and supply chain collaboration
Agricultural management analysis
SRM supplier collaboration
Pre-procurement processes such as supplier registration, qualifications, quotation inquiry, and bidding are fragmented, and there is a lack of unified standards for access and performance management.
Construction methods
Based on supplier master data and a unified approval process, it connects procurement, contract, and settlement scenarios.
Application process
Supplier registration - > qualification review - > quotation inquiry - > supplier selection - > bidding - > performance evaluation
Solution Effectiveness
- Improve supplier admission and management efficiency
- Unified supplier data standards
- Strengthen coordination in procurement preparation
LIMS inspection management
Sampling, testing, review, and report sending involve multiple role processes, making manual transmission prone to omissions.
Construction methods
Through a process center, basic data management, and AI-assisted anomaly alerts, a closed detection loop is formed.
Application process
Entrust and place an order - > Sample collection - > Inspection - > Result entry - > Review - > Report sending
Solution Effectiveness
- Enhance testing standardization and traceability capabilities
- Reduces manual transmission and omissions
- Strengthen the accumulation of testing data
Breeding production management
The chain of breeding, experimentation, feeding, and cost accounting is long, making it difficult to unify process records and operational analysis.
Construction methods
Through business models, rule engines, and mobile capabilities, we undertake planning, breeding, experimentation, breeding, and cost accounting processes.
Application process
Planning - > breeding and matching - > Experimental records - > Feeding management - > Cost accounting - > Business analysis
Solution Effectiveness
- Enhance the level of process refinement
- Provides a data foundation for AI alerts and trend analysis
- Supporting the accumulation of production experience
Equipment and supply chain collaboration
Equipment, materials, supply chains, and contract data are fragmented, making it difficult to promptly grasp supply risks and resource status.
Construction methods
Unify equipment ledgers, materials, suppliers, contracts, inventory, and purchase order objects to form a supply chain collaboration dashboard.
Application process
Demand planning -> Procurement coordination -> Inbound acceptance -> Equipment usage -> Inventory monitoring -> Risk warning
Solution Effectiveness
- Enhancing supply chain transparency
- Reduce supply disruption and inventory risks
- Unified equipment and material management
Agricultural management analysis
It is difficult to timely aggregate data on testing, breeding, supply chain, equipment, and costs, and operational analysis relies on manual data collection.
Construction methods
Unify business indicators, cost targets, and business data, and build agricultural management dashboards and AI data query capabilities.
Application process
Data access - > Indicator definition - > Cost accounting - > Trend analysis - > Decision recommendations - > Execution tracking
Solution Effectiveness
- Improving the efficiency of business analysis
- Support cost and supply risk management
- Forming a unified business view
Business value
From local efficiency improvements to measurable intelligent operations
Synergistic value
Connecting suppliers, contracts, testing, breeding, equipment, and supply chain scenarios.
Efficiency value
Enhance business collaboration efficiency and reduce high-frequency repetitive work.
Data value
Establish a unified data foundation and intelligent analysis capabilities.
Trace value
Make testing, breeding, supply chain, and equipment processes traceable and reviewable.
Replicating value
Quickly replicate industry application templates to more agricultural business units.
Suppliers can manage everything in a unified manner
Entry, quotation inquiry, bidding, and performance are all integrated into the same platform.
Detection can form a closed loop
The entire process from commissioning to reporting is online and standardized.
Production capacity accumulates data
Breeding, experimentation, feeding, and cost formation are all on a continuous record.
Operations can be analyzed intelligently
Aggregate cost, supply, inspection, and production data into a unified business view.
Building a closed-loop AI-native operation in the agricultural industry
Starting from a high-value scenario, accumulate replicable enterprise AI assets and gradually expand to more business units and operational scenarios.