Deploy governable AI agents in ERP systems. Each agent is a trained digital worker with specific business capabilities.

Unlike simple chatbots, Dmmsys AI digital workers can call ERP tools and APIs to execute actual business operations including creating orders, querying inventory, generating reports, and approving workflows.

1)    Support DeepSeek, Qwen, Zhipu and other LLM providers. Agent-level provider configuration with flexible switching.

2)    Each AI agent can bind multiple ERP tools, auto-generating tool_call via JSON Schema for deep AI-business integration.

3)    Cross-agent collaboration (A2A): Sales AI can call Inventory AI to check stock, call Finance AI to calculate prices, enabling multi-agent cooperation.

4)    Every AI call has audit logs recording token consumption, execution time, tools called, and results returned for full traceability.

5)    Cost budget control: set budget limits by agent, project, or department with auto-alerts on overflow.

AI is not a static system frozen at deployment, but a dynamic system that gets better with use. The self-evolution engine achieves continuous capability improvement through a six-phase loop.

After each task execution, the system automatically records the process and results, evaluates effectiveness through multi-dimensional metrics, optimizes parameters and strategies, and auto-deploys better versions after A/B verification.

1)    Six-phase loop: Execute→Track→Evaluate→Optimize→Verify→Deploy, with automatic flow without manual intervention.

2)    Nightly optimization pipeline runs during low-traffic periods, auto-deploying after A/B verification without affecting daytime operations.

3)    Skill versioning: each optimization creates a new version, with rollback to any historical version for safety.

4)    External skill absorption: ecosystem radar scans external MCP servers and skill markets, auto-adapting and absorbing external skills.

5)    Skill health dashboard: monitors call count, success rate, and average effectiveness for all skills.

Transform business requirement documents into runnable ERP module code, dramatically improving development efficiency.

The system includes 62 abstract business patterns, each self-contained with business parameter mapping and code templates covering the most common ERP development scenarios.

1)    Structured requirement input: not free text, but structured six questions plus pattern selection for unambiguous requirements.

2)    Three-phase rendering pipeline: Phase 0 input → Phase 1 semantic matching → Phase 2 AST to code file rendering.

3)    Generated code includes Python models, XML views, CSV permissions, and demo data, ready to install and use.

4)    Support custom pattern extension: enterprises can register their own patterns as templates for reuse.

Every AI call is fully traceable end-to-end, from user input to LLM call to tool execution, with complete records at each step.

Records not only technical information (tokens, duration, status) but also business information (which tools called, which data operated), achieving true transparent governance.

1)    Three-layer audit logs: AI audit log, LLM call log, tool call log, recording information at different levels.

2)    Cost budget control: budgets by agent, project, department with overflow alerts.

3)    Data masking: auto-mask sensitive data before AI calls, audit logs don't leak sensitive information.

4)    Alert rules: auto-alert on token limit, response timeout, exceptions, with configurable conditions and notifications.

5)    Value-based billing: measurable values plus coefficients plus calculation services plus audit logs for precise AI value measurement.

AI math platform for research and education, with formal theorem knowledge base, massive structured problem bank, and formula library with multi-format rendering.

Not a simple calculator, but a complete math knowledge system with theorem proving, natural language Q&A, proof verification, and learning training.

1)    762K structured theorems/propositions/proofs, 18.5M dependency relations, 772K proof steps.

2)    1.38M math problems covering 15 subject subcategories, 6 problem types, with structured conditions/targets/paths.

3)    2.11M logical formulas with 4 rendering formats: Unicode, LaTeX, OpenMath content tree, Chinese natural language.

4)    Natural language math Q&A: 5 intent types, bilingual, zero-hallucination design with deterministic queries.

5)    Proof verification: 5 fake-proof flags, three-state results, counterexample support.

Drag-and-drop flow designer with full BPM capabilities, designing complex approval flows without development.

Supports conditional branches, parallel execution, subprocesses, timers, suspend/resume and other advanced flow features.

1)    AntV X6 visual designer with drag-and-drop operation, no programming required for complex flows.

2)    15 event hooks for flexible flow behavior extension, supporting custom logic at various flow stages.

3)    Work item 7-state lifecycle management, fully tracking each work item's state transitions.

4)    Flow variables support char/int/float/boolean/date/json/reference types.

5)    Support subprocesses, suspend/resume, timers for complex business scenarios.