When Data+AI Meets B2B: How GBase Breaks the “Deploy-and-Stall” Curse?

In recent years, “Data+AI” has become a buzzword in enterprise digital transformation. However, in the B2B sector, the real-world challenges of implementation are far more complex than many anticipate. Data is scattered across various systems, creating isolated silos; business units feel that technology teams don’t understand their needs, while tech teams struggle to extract clear requirements from the business side. Significant resources are invested, only to end up with a model that “no one uses.” This is a familiar story for many organizations.

So, how can Data+AI be effectively implemented in B2B scenarios to minimize detours and deliver real results?Drawing on over two decades of deep experience in the database field, GBase aims to combine its product practices to outline a pragmatic implementation roadmap.

Prerequisite: Solidify the Foundation to Address Core B2B Pain Points

Many enterprises rush to launch AI projects, only to find their data is fundamentally unusable—quality is poor, security is not guaranteed, and business stakeholders don’t buy in.

It’s like building walls without first laying the foundation.

How should the foundation be built?

GBase’s practical experience points to a four-step approach:

No.1 Consolidate Your Data

Enterprise data is scattered across different systems and formats—some in relational databases, others in files, and some as time-series data. GBase GCDW’s multi-model and multi-modal architecture can manage all this dispersed data in a unified manner. Row store, column store, in-memory, text, time-series, graph—all can be incorporated on a single platform, fundamentally resolving the data silo problem. This multi-model, multi-modal data serves as the essential “raw material” for AI model training, and unified storage provides a complete data view for subsequent feature engineering and model building.

No.2 Cleanse Your Data

Data quality directly impacts the effectiveness of AI models. The built-in data governance capabilities of GBase 8a automate data quality verification, cleansing, and repair. It also supports data lineage tracing and metadata management. Enterprises simply need to define their requirements, and the platform handles the data processing. This ensures the data fed into AI models is high-quality “clean fuel,” preventing the “garbage in, garbage out” problem.

No.3 Lock Down Your Data

In industries such as finance, telecommunications, and government, data security is a non-negotiable red line. GBase is the first database to receive a commercial cryptographic product model certificate from China’s State Cryptography Administration. It has passed Level 4 information security protection evaluation and dual evaluation by the State Cryptography Administration, and is also the only database product to obtain the 3C certification from the General Administration of Quality Supervision, Inspection and Quarantine. Technologies such as fine-grained encryption, dynamic data masking, and access control provide “static leakage prevention and dynamic exposure control,” fully meeting compliance requirements. This establishes a secure foundation for compliant AI usage.

No.4 Develop Your People

Beyond building a solid data foundation, the human factor is equally critical. GBase provides full-cycle capability-building support for enterprises:

• For technical teams, GBase offers specialized training on its database product series covering AI algorithm integration, multi-model database management, and data governance, enabling engineers to rapidly acquire the skills needed for Data+AI implementation.

• For business teams, foundational data and AI knowledge training enhances data awareness and application capabilities among business personnel.

• For small and medium-sized enterprises, they can directly rely on GBase database products and technical services without the need for in-house development, significantly reducing the cost of building internal capabilities.

Pilot Implementation: Start Small, Run Fast, Validate Value, and Iterate

Once the foundation is solid, can you roll it out across the entire organization?

GBase’s advice is: Start with a “small incision” pilot to validate.

How to choose a pilot scenario? Three criteria:

• The business pain point is prominent enough (e.g., high false alarm rate in risk control, severe user churn).

• Data is accessible and manageable.

• Results are quantifiable and measurable.

How to advance the pilot? Three steps:

No.1 Define the Scope

It is not advisable to deploy across the entire group at the outset. Instead, select a single branch or business line to start, for example, “credit risk control for a specific branch.” The high security and performance of GBase 8s are well-suited to support such scenario requirements.

No.2 Build the Model

Building models becomes simpler and more efficient with GBase as the foundation:

• Data Preprocessing: Directly extract the feature data required for the pilot scenario. Leverage the native data cleansing and feature engineering capabilities within GBase to automatically filter core features—such as user transaction features for financial risk control or user behavior features for telecom alerts—eliminating the need for additional complex tool development.

• Model Selection and Training: Prioritize mature and easily deployable algorithms, using the native “GBase + AI” integration paradigm. For unstructured data scenarios like image recognition and semantic search, GBase GCVD vector database provides high-dimensional vector storage and similarity search capabilities, serving as the underlying storage engine for a private AI knowledge base to support applications such as intelligent Q&A and image-based search. Through small-batch iterative training, model parameters are rapidly optimized to ensure precision and business alignment.

• System Deployment and Debugging: Employ lightweight deployment to integrate the model into existing business systems (CRM, ERP, etc.). GBase supports deployment on physical machines, virtual machines, containers, and cloud platforms, with elastic scaling to accommodate workload fluctuations. It is compatible with Oracle, PostgreSQL, and MySQL, quickly resolving integration issues with existing systems to ensure stable operation.

No.3 Evaluate Results

After the pilot runs successfully, the actual results must be comprehensively assessed against the pre-defined quantitative metrics: How much has the identification accuracy of the risk control model improved? How much have operational costs been reduced? These must be supported by clear data.

At the same time, feedback from business personnel is equally important. Is the AI model user-friendly? Is the workflow too cumbersome? Are false positive rates within acceptable limits? Only by combining data metrics with the business experience can we identify the real issues hindering AI implementation.

Once problems are identified, the capabilities of GBase enable rapid optimization, allowing the AI model to continuously evolve:

• Model false positive rate too high?

This indicates room for improving the AI model’s precision. You can leverage GBase’s massive data storage capacity to supplement higher-quality training samples and utilize its feature engineering functions to optimize feature selection, enabling the model to learn from more data and achieve better accuracy.

• Response speed too slow?

This signals a need to enhance the efficiency of AI inference. You can optimize the vector index structure of GBase GCVD and adjust distributed computing resource allocation to make vector retrieval faster and AI model responses more timely.

• Workflow too complex?

This suggests the interaction between the AI tool and business users needs improvement. The visual interface of GBase can be refined to lower the barrier for business personnel using AI models, ensuring technology truly serves people.

This closed-loop process of “evaluate, identify problems, optimize AI” is precisely where the value of GBase’s flexible configuration lies—ensuring the AI pilot stays on track and the model becomes more accurate with each run.

Scaled Replication: Standardized Enablement to Break the “Deployment Bottleneck”

After a successful pilot, the next step is replication and scaling. However, different business units have distinct characteristics, so a simple “copy and paste” often fails.

GBase’s approach: Solidify the pilot experience into a “template,” not a “mold.”

Three Standardizations:

• Technical Solution Standardization: The deployment architectures, data governance processes, and model selection criteria for GBase 8c, 8s, 8a, and GCDW are all solidified into standard blueprints. When applied to different scenarios, only minor parameter adjustments are needed, avoiding repetitive development.

• Business Process Standardization: Map out the implementation workflow from the pilot scenario to create a standardized operation manual. When each business unit follows this manual, it ensures consistency in deployment outcomes.

• Compliance Standardization: Compliance requirements vary across finance, government, and telecom sectors. GBase products feature built-in national cryptographic certification and Level 4 information security protection capabilities, paired with standardized security configuration processes to ensure compliance is controllable during large-scale replication.

A Three-Layer Path for Scenario Expansion:

• Same-Type Scenarios: Once credit risk control is successfully deployed, credit card risk control and wealth management risk control can be directly reused.

• Cross-Business Unit: Once intelligent operations for one production line are validated, they can be promoted across the entire group.

• Full Business Chain: Extend from risk control to customer profiling, precision marketing, and intelligent customer service, enabling empowerment across the entire chain.

Final Step: Platformization

Integrate all capabilities into a unified platform—using GBase 8a as the core data foundation, combining multi-model data storage, data governance, and data security capabilities. Based on GBase's AI integration capabilities, build an AI model platform covering the entire lifecycle of model development, training, deployment, and iteration. Additionally, the platform can integrate the GCVD vector database as the storage engine for a private AI knowledge base, providing efficient support for scenarios like intelligent Q&A and enterprise knowledge retrieval.

Furthermore, by integrating business empowerment and compliance control modules, a unified management system of “Data-Model-Business-Compliance” is achieved. All capabilities run natively on GBase, eliminating the need for third-party tool integrations. Business users can independently call models, query data, and generate reports through a visual interface, dramatically improving implementation efficiency. GBase GCDW’s disaggregated storage and compute architecture supports elastic scaling based on business demands; GBase is fully compatible with China’s domestic basic hardware and software, offering an open ecosystem with strong extensibility.

Long-Term Operations: Continuous Iteration for Maximum Value

Data+AI implementation is not a one-time effort. Businesses evolve, data changes, and technology advances; the operational mechanism must keep pace.

GBase’s recommendation: Establish three “ongoing” mechanisms

1. Ongoing Data Operations

GBase GCDW’s HTAP capabilities enable a sub-second closed loop of data collection, processing, and analysis, ensuring the AI model always uses fresh data. Real-time data quality monitoring triggers automatic alerts and repairs for anomalies.

2. Ongoing Model Operations—Enhancing Iteration Efficiency with Vector Database

AI models have a “timeliness” factor; as business and data evolve, model accuracy gradually declines. GBase establishes a mechanism for ongoing model operations:

• Real-Time Model Monitoring: Leveraging intelligent monitoring features to track model response times, accuracy, false positive rates, and other metrics in real time, with a performance alerting mechanism that automatically triggers an optimization process when indicators drop below thresholds.

• Model Iteration and Optimization: Regularly collect business feedback and new data, utilizing the distributed computing and massive storage capabilities of GBase GCDW to conduct iterative model training, optimizing parameters and feature engineering. General Data Technology continuously tracks AI technology developments, incorporating cutting-edge algorithms like DB-GPT and Chat-DB as appropriate to maintain model advancement. During iteration, the GCVD vector database can be fully utilized to store feature vectors of historical versions, accelerating similar sample recall and model comparison.

• Model Version Management: Using GBase’s version management and data snapshot capabilities to record the parameters, effects, and applicable scenarios of different model versions. If a new version encounters issues, a quick rollback to a stable version ensures business continuity.

3. Ongoing Value Review

Every quarter and semi-annually, conduct data analysis on the deployment results using GBase 8a to evaluate actual value. For areas that fail to meet expectations, review the reasons, adjust strategies, and continuously optimize.

Key Success Factors and Pitfall Avoidance Guide

Combining the practical experience of GBase, the key success factors and risk mitigation strategies for Data+AI implementation in B2B become more targeted.

For Data+AI to truly deliver results in the B2B sector, the following five factors are critical:

• Business-Driven: All efforts must revolve around business pain points. No matter how advanced the technology, if it doesn’t solve real problems, it’s futile. GBase’s value lies in its full-scenario adaptability, precisely turning business requirements into reality, avoiding both AI for AI’s sake and neglecting to use AI where it’s beneficial.

• Data as the Foundation: Data is the fuel for AI. GBase 8c integrates scattered data, GBase 8s safeguards sensitive information, and GBase 8a or GCDW delves deep into massive data analytics. Only when data is “available, trustworthy, and secure” can AI deliver its potential. This is the prerequisite for all work.

• Start Small, Run Fast: Don’t try to boil the ocean. Begin with a small scenario, validate and iterate quickly, then scale after success is proven. GBase’s lightweight deployment and flexible configuration keep trial-and-error costs under control and make successful experiences replicable.

• Cross-Functional Synergy: Tech and business teams cannot work in silos. Establish a cross-departmental collaboration mechanism where business personnel are involved from the pilot stage. Combined with GBase’s full-process technical support and training, this ensures both sides truly communicate, making the model usable, effective, and genuinely adopted.

• Sustained Operations: Going live is not the finish line. As the business and data evolve, so must the models. GBase’s intelligent ops and continuous iteration capabilities support the three ongoing mechanisms of data operations, model operations, and value review, enabling AI to deliver sustained value rather than “deploy and stall.”

Based on GBase’s practical experience, here are five common risks and corresponding strategies:

Risk 1: Data Security Breaches

Strategy: GBase’s national cryptographic certification + Level 4 security protection + fine-grained security safeguards provide full-process control from encryption and masking to auditing. Regular compliance audits and integrated blockchain anti-tampering technology ensure data security.

Risk 2: Technology Built but Not Used by Business

Strategy: Involve business personnel from the pilot phase. GBase’s visual interface and low-code capabilities lower the adoption barrier for business users. Paired with specialized training, this eliminates the “unusable” concern.

Risk 3: Unclear ROI

Strategy: Define quantitative metrics when selecting the scenario. GBase’s high performance and cost-efficiency optimize resource allocation. Conduct regular reviews to ensure ROI is measurable and demonstrable.

Risk 4: Reinventing the Wheel for Every Scenario

Strategy: Standardized templates + platform-based enablement. GBase’s distributed architecture and data-sharing capabilities support the mass reuse of technical solutions and business processes, avoiding redundant development.

Risk 5: Unable to Keep Up with Tech Iteration

Strategy: GBase continuously tracks the frontiers of AI and database technology. New capabilities like vector databases and DB-GPT will be gradually integrated into the product suite. Running your business on GBase means you are always positioned at the leading edge of technological iteration.

The implementation of Data+AI in the B2B domain has never been a one-sided question of whether “the technology is advanced.” It is a systematic engineering challenge of whether it can “truly solve business problems.” It requires coordination across data, technology, business, and organization, all firmly underpinned by a reliable data foundation. GBase will continue to iterate and upgrade, deeply integrating AI technology to become the core engine driving Data+AI implementation for B2B enterprises—helping them reduce costs, boost efficiency, innovate business models, build core competitiveness, and propel new quality productive forces.