Intelligent Upgrading and Transformation Project: AI Bidding Document

Client's Challenges and Pain Points

For most enterprises, it is a crucial requirement to address their own business problems accurately. In order to improve the accuracy of general-purpose large models in solving business problems, enterprises usually adopt two main approaches:

Industry-Specific Fine-Tuning of General-Purpose Models: By fine-tuning general-purpose large models with specific data, the models are better aligned with the enterprise's business scenarios and requirements.

RAG: Through knowledge externalization, enterprise-specific proprietary knowledge is integrated with general-purpose large models to enhance the models' response accuracy.

Regardless of which approach is adopted, it is inseparable from the integration of the high-quality data. This kind of data is often hybrid-type and multi-modal, covering the following categories:

Structured Data: Typically processed through completed data infrastructure (Data Infra), it includes ordered data generated by business systems.
Semi-structured Data: Such as logs, configuration files, XML, etc.
Unstructured Data: Including documents, images, videos, etc., which are often not standardized.

Such multi-modal and multi-type data requirements pose significant challenges to enterprises in the implementation of GenAI technology, manifested specifically in multiple links such as data integration, storage, cleaning, and annotation.

Therefore, when advancing the implementation of GenAI, enterprises not only require breakthroughs at the technical level but also need to establish a completed and brand new data governance system to ensure the efficient circulation and secure use of data. Only in this way can we truly unlock the potential of GenAI in enterprise business scenarios and drive the manufacturing industry toward a more intelligent and efficient future.

Objectives

Based on the existing Hadoop data middle platform, construct an AI data platform powered by MatrixOne Intelligence.

Achieve unified governance of structured and unstructured data, and enhance data usability and intelligence level.

Build demonstrative agent applications based on processed and governed internal and external data to demonstrate the practical value of AI in business scenarios.

MatrixOrigin's Solution

MatrixOne Intelligence is an AI data intelligence solution for multi-modal data, designed to help enterprises address challenges such as data fragmentation, the complexity of multi-modal data integration, and difficulties in implementing generative AI applications. The solution aims to transform enterprises' internal proprietary data into AI-Ready data that can support the implementation of GenAI applications and generate business value. Essentially, this goal is to improve the accuracy of large models in enterprise application scenarios.

As shown in the figure below, the solution is divided into 3 layers from bottom to top: the Data Integration and Governance Layer, the Database and AI Service Layer, and the Application Interaction Layer. These layers are interconnected, jointly forming a robust AI data intelligence solution.

Core Tasks

1. AI Data Platform Construction

Establish an end-to-end architecture covering data ingestion, governance, storage, parsing, feature engineering, as well as model training and inference.

Achieve intelligent governance of structured and unstructured data, including the access and processing of new types of data sources such as web pages, documents, audio and video, and code.

Optimize data transmission and storage performance to ensure the platform's stability and efficiency under high load.

2. Development of Demonstrative Agent Applications

Bidding Document Preparation Agent: Realize the automatic generation and intelligent parsing of bidding and tendering documents.

Knowledge Base and Training Assistant: Intelligently extract and integrate enterprise knowledge, automatically generate training plans, and improve training effectiveness.

Supply Chain Assistant: Conduct demand forecasting and supply chain risk early warning based on historical data and real-time monitoring.

Expected Outcomes

The AI data platform is fully launched, enabling data governance and intelligent parsing.
Multiple demonstrative agent applications are successfully deployed, verifying the effectiveness of AI capabilities in various business scenarios.
Data and model foundations are consolidated, laying a solid foundation for in-depth applications in subsequent phases.

Solution for the AI Bidding Document Agent

1. Pain Points and Challenges

1.1 Low Efficiency in Historical Performance Data Management and Difficulties in Review

Difficulties in Retrieving Dispersed Files: Historical performance materials such as contracts and invoices are stored dispersedly, resulting in inefficient manual retrieval and high risk of omissions.
Tedious Handling of Sensitive Information: Requires repetitive operations such as desensitization and image compression for massive files.
Over-reliance on Manual Work for Review and Verification: Manual verification of historical contracts is necessary when customer/supplier fields change, due to the lack of automated comparison tools.

1.2 Problems about Cross-departmental Collaborate and Data Integrity

High Coordination Costs Across Departments: Repeated communication is required for technical solutions, qualification certificates and other content, which is prone to delaying progress.
Risk of Missing Key Documents: Lack of sealed documents, authorization letters and other materials may directly lead to bid invalidation.
Barriers to Reusing Historical Data: No standardized filing system for bidding document cases has been established, resulting in low reuse rate.

1.3 Format Compliance and Version Control Risks

Stringent Requirements for Format Details: Formatting errors such as incorrect headers/footers or table of contents indexing may directly cause bid invalidation.
Compatibility Issues with Electronic Bids: Factors such as file formats and signature encryption may lead to system submission failures.
Version Chaos in Multi-person Collaboration: Document version conflicts may result in content confusion or omissions.

1.4 Blind Spots in Legal Clause Compliance and Qualification Conformity

Insufficient Identification of Special Clauses: Hidden requirements such as environmental standards and localization commitments are easily overlooked.
Lack of Legal Risk Prediction: Inadequate review of clauses such as contract penalties and intellectual property ownership.
Uncontrolled Management of Qualification Validity: Use of expired licenses or authorization documents leads to qualification review failure.

1.5 Mismatch in Technical Solutions and Imbalance in Professional Expression

Deviations in Requirement Understanding: Ambiguous expressions in bidding documents result in insufficient pertinence of solutions.
Conflict Between Innovation and Feasibility: Difficulty in balancing the demonstration of technical advantages and implementation risks.
Dilemma in Professional Terminology Expression: Technical descriptions tend to fall into the polarization of obscurity or over-simplification.

2. Technical Difficulties in Bid Document Preparation

High complexity in the integration of core key technologies, including deeply coupled multi-source heterogeneous data architecture, intelligent parsing of massive unstructured data, and adaptation of various AI large model algorithms.

Involves enterprises' massive structured and unstructured data, requiring an unconventional large-scale data governance engineering.

Multi-modal data parsing and recognition based on existing large models may lead to distortion and hallucination, necessitating fine-tuning and optimization of the models.

3. Technical Architecture of Bid Document Preparation Agent

An end-to-end process architecture encompassing multimodal data access, governance, storage, parsing, feature engineering, and model training & inference is established through MatrixOne Intelligence.

4. Workflow of Bid Document Preparation Agent

Full-process closed-loop management of the business is achieved through in-depth AI analysis, sensitive information processing, and end-to-end bid document generation.

5. Data Governance Solution

Unlock Data Value to Drive Business Innovation: Integrated governance of multimodal data (documents, image data, etc.) supports the implementation of AI applications and intelligent decision-making.

Enhance Operational Efficiency and Automation Level: Over 80% of enterprise data is unstructured and not effectively utilized; data governance can reduce data "dark matter".

Establish Data Assets and Knowledge Base: Extract entity relationships from unstructured data to form an enterprise knowledge base and accelerate innovation.

6. Benefits and Value of the Bid Document Intelligent Agent Project

Based on an enterprise-level knowledge base as the foundation, the Bid Document Intelligent Agent runs through the end-to-end process of "data ingestion — key point extraction — framework generation — consistency check — human review closed loop — one-click export". The system can access multi-source materials such as previous tender documents, past contracts, and financial statements, automatically extract compliance and scoring key points, generate the chapter structure and templated text required for the final draft, and automatically aggregate qualification and financial data in the background to ensure unified standards. Experts only need to provide revision suggestions for key paragraphs to quickly finalize the draft.

In practical implementation, the average daily bid preparation time per person is reduced from hours to minutes, with a significant decrease in rework and communication costs. The system has been specially adapted to meet the scenario requirements of bid documents, enabling stable generation of compliant bid text and reducing risks caused by inconsistent expressions or format deviations. Meanwhile, through the continuous accumulation of clause libraries, template libraries, and historical cases, experience is assetized, allowing new employees to get up to speed faster and training expenses to be reduced accordingly.

From an organizational perspective, bid document preparation is transformed from "relying on experience" to "relying on a system", resulting in shorter delivery cycles and controllable quality. This frees up the team's time for bid strategies, differentiated solutions, etc., substantially enhancing bid winning competitiveness and compliance levels.