Inconsistent Metric Definitions
The same metric can mean different things across teams and systems, causing BI reports and AI answers to conflict.
MetaAGI: An Enterprise Semantic Brain That Understands Business
MetaAGI compiles metrics, rules, processes, and experience from documents and systems into machine-readable ontology. AI Q&A, business diagnosis, and merchandise strategy can share one business language with traceable answers designed to minimize hallucinations.
Deep Business SemanticsBuild a unified semantic knowledge base with enterprise ontology for products, stores, associates, and operating rules.
End-to-End AI LoopConnect AI diagnosis, strategy recommendations, execution, attribution review, and continuous model learning.
Enterprise-grade Security and ComplianceUse tenant isolation, RBAC and ABAC access controls, TLS encryption, and sensitive-data masking to help keep data within its approved environment.
Analyze the top 10 East China stores by sell-through last month.
☼Thinking⌃
Aligning store ontology and sell-through metrics
☼Illustrative workflow⌄
East China store rankings are complete, with metric source and calculation scope attached.
- Nanjing Xinjiekou78.5%
- Hangzhou Hubin76.2%
- Suzhou Center74.8%
- Data sourcedws_store_month
Trace: metric source dws_store_month, scope = monthly store sell-through.
How should we handle broken-size SKUs in these stores?
☼Thinking⌃
Querying mid-season merchandise SOP and inventory rules
☼Illustrative workflow⌄
Mid-season merchandise SOP is linked, and actions are generated by inventory and sales rules.
- Stock <= 5, high salesReplenish by transfer, prioritizing nearby stores with higher stock.
- Stock > 20, low salesClear through promotion to reduce stock pressure and recover display space.
- In betweenConsolidate display and add to the next merchandise adjustment list.
Trace: rule source Mid-season Merchandise SOP, Chapter 3; actions = transfer/promotion/delist.
Why did inventory turnover slow in South China this month? Diagnose and summarize.
☼Thinking⌃
Joining metrics with inventory rules
☼Illustrative workflow⌄
The slowdown is mainly from high-stock, low-moving products and replenishment rhythm drift.
- 1. UnderstandIdentify region, period, turnover metric, and diagnostic intent.
- 2. AnalyzeQuery stock, sales, and replenishment rules through ontology mappings for explainable attribution.
The diagnosis includes field-level traceability and recommended actions.
Based on the new-product launch SOP, create next week's East China merchandise plan.
☼Thinking⌃
Routing SOP documents and merchandise metrics
☼Illustrative workflow⌄
The launch SOP is read and combined with regional performance to generate an execution list.
- 1. Match rulesIdentify new products, region, launch cycle, and action rules.
- 2. Build listOutput product tiers, stocking, display, and review milestones with owner assignment.
Execution results can flow back to continuously update enterprise semantic memory.
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Why Enterprises Need MetaAGI
Translate scattered data, documents, and experience into reusable business semantics that AI can understand and execute.
Dormant Document Knowledge
Operating knowledge is locked in Word, Excel, and PPT files, making it hard to find, understand, align, or transfer.
Untrusted AI Answers
General-purpose LLMs lack enterprise context, so they may hallucinate, miss traceability, and fail decision-grade trust.
Fragmented Applications
Each AI app redefines products, stores, and metrics. One business-definition change must be synchronized repeatedly.
Core Capabilities
A complete loop for ontology parsing, semantic mapping, intelligent Q&A, and knowledge governance.
Ontology Parsing Agents
Multi-agent pipelines decompose metrics, rules, processes, and business objects automatically.
7 AgentsDisambiguationGap audit
Turn knowledge scattered across documents and experience into manageable, publishable ontology assets.
Eight Ontology Elements
Describe business semantics through eight standard enterprise elements.
StructureSemanticsSecurity
Unify objects, attributes, relationships, value types, metrics, rules, actions, and security.
Ontology-Warehouse Mapping
Map business terms deterministically to physical tables, fields, and data models.
Master tablesField mappingRouting
Let LLMs query through published mapping paths instead of guessing tables or drifting definitions.
Document Parsing and Routing
Extract semantic elements from metrics, SOPs, and operating reports.
Docs to ontologyIntent routingTraceability
Route questions to knowledge bases and warehouses by intent, then return trusted answers with traceability.
Business Value
Democratize business semantics for better decisions.
Unified Business Language
Use one enterprise ontology to define products, stores, members, metrics, rules, and actions across departments.
Reusable Knowledge Assets
Structure operating knowledge so it survives employee turnover, can be reused across teams, and can be adapted to new industries through a shared semantic foundation.
Trusted AI Enablement
Provide traceable answers grounded in published ontologies, mappings, and knowledge bases, designed to minimize hallucinations.
Reusable Application Semantics
Let Q&A, diagnosis, and merchandise strategy share one semantic foundation instead of rebuilding definitions per app.
Product Architecture
Connect applications, knowledge assets, and data foundations through enterprise ontology.
Ontology Modeling as the Core, Business Applications in Action
The ontology modeling center provides one semantic foundation for intelligent Q&A, Store Intelligence, Omnichannel Allocation, and other upper-layer applications.
Ontology Modeling Center
Business Entity Management
Visual modeling and versioning for objects, attributes, relationships, and value types with AI extraction and human curation.
Metric and Rule Engine
Define metric scope, formulas, dimensions, periods, and business rules consistently across systems.
Semantic Memory Layer
Manage synonyms, aliases, history, and source traceability so concepts stay aligned.
Ontology-Warehouse Mapping
Map objects, attributes, metrics, relationships, rules, and actions to physical tables and fields.
Intelligent Document Studio
Upload metrics, SOPs, and reports, then let AI extract ontology candidates and mappings.
Permission and Governance
Use knowledge access, data permissions, audits, and rollback to govern assets safely.
Key Mechanisms
Ontology breakdown, semantic mapping, and governance ensure trusted business understanding.
Automated Ontology Breakdown
Multi-agent pipelines handle parsing, disambiguation, confidence scoring, completeness checks, and human review.
Semantic-to-Physical Mapping
Map ontology objects, metrics, rules, and relationships to warehouse fields for deterministic data routing.
Security and Version Governance
Protect semantic assets through data permissions, knowledge access, audits, and version rollback.
Built For
Lower the barrier to business semantics for headquarters, merchandise, store, and data teams.
Business VPs / Directors
Definitions vary across teams
Unify metrics, rules, and processes through enterprise ontology.
Merchandise Operations
Stocking decisions are fragmented
Use traceable merchandise metrics and recommended actions.
Store Managers
Daily data questions need manual work
Ask in natural language and receive trusted operating data.
Regional Managers
Multi-store diagnosis is slow
Diagnose stores quickly with a unified business ontology.
Data Analysts
Metric and field alignment repeats
Translate business semantics into deterministic physical queries.
Digital and IT Teams
New apps rebuild semantics
Reuse one semantic foundation for more intelligent applications.