DI-ACE TechnologyDiscuss an AI project
Services

Make AI an operational business capability

From scenario judgment and solution design to system delivery and continuous operation, we help enterprises promote AI applications in a controllable and integrated manner.

Core services

From judging the direction to entering the production environment

Each service is built around a clear business problem and incorporates permissions, data, systems and operations into the scope of delivery.

01 / Strategy

AI consulting

Identify high-value scenarios around business goals and clarify the implementation path for collaboration between data, systems, models and organizations.

what do we solve

Enterprises often have no shortage of AI ideas. What’s really difficult is judging which scenarios are worth prioritizing investment and how to control pilot scope and implementation risks.

Delivery content
  • Business scenario sorting and value ranking
  • Data and system condition assessment
  • AI capability roadmap
  • Pilot Scope and Acceptance Criteria
Typical scenario
  • Enterprise AI planning
  • Intelligent assessment of inventory systems
  • Knowledge and data asset inventory
  • AI project feasibility verification
02 / Build

AI application development

Encapsulate model capabilities into usable, maintainable, and scalable enterprise applications and business workbench.

what do we solve

There are still engineering gaps in permissions, stability, data access, logs, and user experience between the demonstration effect and the production application.

Delivery content
  • Application prototyping and interaction design
  • Access to models and knowledge capabilities
  • Business system interface integration
  • Deployment, monitoring and operation and maintenance solutions
Typical scenario
  • Intelligent question answering and knowledge retrieval
  • Content generation and review
  • Business assistance workbench
  • Internal management and collaborative applications
03 / Agent

Agent construction

Connect models, tools, data, and processes to enable AI to complete multi-step business tasks within clear boundaries.

what do we solve

Agents need not only conversational capabilities, but also tool invocation, status management, manual confirmation, exception handling and complete auditing.

Delivery content
  • Task and tool boundary design
  • Process orchestration and status management
  • Manual approval and exception rollback
  • Operation log and effect evaluation
Typical scenario
  • Work order processing assistant
  • Report generation assistant
  • Cross-system information entry
  • Automated execution of operational tasks
04 / Modernize

Intelligent enterprise systems

Embed AI in existing ERP, CRM, OA and business systems to reduce duplication of operations and improve information utilization efficiency.

what do we solve

Enterprise systems have accumulated a large amount of business data and processes, but information is scattered and operations are repeated, making it difficult for traditional functions to provide natural language and automatic decision-making capabilities.

Delivery content
  • Existing system and interface analysis
  • AI capability embedding solution
  • Data permissions and operational controls
  • Grayscale launch and continuous optimization
Typical scenario
  • ERP and CRM intelligent assistance
  • OA Approval and Material Processing
  • Multi-system data summary
  • Upgrading the existing application experience
Delivery Process

Starting from verification, gradually expand business scope

01

Scenario diagnosis

Determine priority scenarios based on business frequency, value, data conditions and risk boundaries.

02

Solution verification

Use small-scale prototypes to verify answer quality, process feasibility, and system access methods.

03

Project delivery

Complete permissions, logs, exception handling, deployment and operation and maintenance capabilities to enter the real business environment.

04

continuing operations

Continuously optimize models, knowledge and processes through feedback, reviews and business data.

Engineering Principles

The foundation of enterprise AI is not models, but control capabilities

Permission isolation

Inherit enterprise organizations, roles, and data permissions to prevent models from bypassing existing access boundaries.

Leaving traces throughout the process

Record input, knowledge sources, tool calls, output results and manual operations to support audit traceability.

Manually controllable

Set up confirmation, approval and rollback mechanisms for high-risk operations, and clarify the boundaries of responsibility between AI and humans.

Flexible deployment

Choose cloud, dedicated environment or privatized deployment method based on data security and system environment.

sustainable evolution

Connect models, knowledge and systems in a modular manner to facilitate subsequent replacement, expansion and optimization.

The right team

When companies prepare to turn AI into long-term capabilities

  • Enterprises that have clear business processes and hope to find an entry point for AI
  • Teams that already have software systems and want to add intelligence capabilities
  • Knowledge, customer service, data or process intensive business
  • Organizations that value permissions, security, auditing, and long-term maintenance