Transparent material costs in development: How AI reveals hidden cost drivers in purchasing

In many industrial companies, the biggest costs arise not in purchasing, but already in development. Decisions about materials, component geometries, or variant structures have a significant impact on subsequent production and procurement costs. At the same time, there is often a lack of reliable data for realistic cost estimates in the early stages of a project.

With PriceGuard AI, Kleen Software is developing an AI-based platform that analyzes material data, makes cost structures transparent, and identifies economic risks at an early stage.

The challenge: Lack of cost transparency in early development phases

In many companies, relevant information about components and materials is spread across different systems. Typical data sources include PLM systems, ERP solutions such as SAP, technical drawings, and historical purchasing data.

However, this data is often:

• Spread across multiple systems
• Historically grown and inconsistent
• Incompletely maintained
• unstructured and incomparable with each other

At the same time, there is increasing pressure on companies to provide reliable cost estimates early in the development process. Purchasing, development, and controlling departments need a valid basis for decision-making in order to avoid budget risks and make economical product decisions.

Manual analyses quickly reach their limits here. They are time-consuming, fragmented, and heavily dependent on the experience of individual persons.

Our solution: PriceGuard AI

PriceGuard AI is a multimodal AI platform for automated analysis of material data and precise cost estimation of purchased parts.

The platform consolidates data from various enterprise systems and links it to technical information such as drawings, specifications, or 3D geometries. Even incomplete or non-standardized data sets can be processed and supplemented by artificial intelligence.

On this basis, PriceGuard AI creates:

• Transparent cost estimates
• Structured material analyses
• Early identification of cost drivers
• Systematic data quality assessments

This provides a reliable basis for decision-making in development, purchasing, and controlling – even in the early stages of a project.

Technological foundations and functionality

PriceGuard AI is based on a multimodal AI architecture that combines technical data, documents, and historical price structures.

Data collection and consolidation

The first step is to consolidate material information from various company systems. This includes PLM systems, ERP data, technical documents, and historical price structures.

This data forms a uniform basis for analysis for all further steps.

Geometry and document analysis

The platform can automatically read technical drawings, specifications, and 3D models. Relevant properties such as dimensions, materials, or structural features are recognized and integrated into the analysis.

Intelligent data enrichment

Missing or incomplete attributes are supplemented by AI-supported processes. To do this, the system compares existing components with historical reference values and similar materials.

Clustering of comparable materials

Similar components are automatically grouped together. This allows cost structures within material groups to be analyzed and compared.

AI-based cost estimation

Based on this data, PriceGuard AI calculates realistic cost ranges for materials, manufacturing processes, and component structures.

Transparent presentation of results

All assumptions, reference values, and calculations are presented in a comprehensible manner. This results in explainable analyses instead of opaque black box models.

Additional features

PriceGuard AI offers a range of additional analysis features:

• Multimodal data processing: Analysis of 2D drawings, 3D geometries, specification documents, and metadata
• Outlier detection: Identification of unusual price structures within comparable material groups
• Automatic data enrichment: Supplementing missing attributes with AI-based comparison models
• Transparent analysis reports: Fully traceable cost models and decision-making bases
• High data security: Can be used on your own AI server
• Flexible Integration: Use as a platform, CAD plugin, or analysis module in the development process

Application examples and customer benefits

PriceGuard AI is particularly suitable for companies with complex material structures and large component portfolios.

Typical areas of application are:

Product development
Early cost transparency for new components and variants.

Strategic purchasing
Better basis for negotiations thanks to transparent cost analyses.

Controlling
Reliable forecasts and early identification of economic risks.

Technical data management
Structured analysis of historical material data and continuous improvement of data quality.

Automated analysis enables realistic cost estimates to be made early on in project phases. At the same time, outliers, redundant components, or inefficient material structures can be identified at an early stage.

Advantages at a glance

• Precise cost estimates based on real company data
• Early detection of cost drivers and economic risks
• Transparent derivation of all analyses and calculations
• Sustainable improvement in material data quality
• High data security thanks to on-premise AI servers
• Flexible integration into existing development and purchasing processes

Strategic perspective

PriceGuard AI fundamentally shifts cost transparency within the company.

Instead of reactive post-calculation, a proactive, data-based decision-making basis for development, purchasing, and controlling is created.

The results are:

• More informed investment decisions
• Greater planning reliability
• Stronger cooperation between technical and economic areas
• Better long-term data quality within the company

Even moderate improvements in material costs can have a significant impact on operating results for large procurement volumes.

Conclusion

With PriceGuard AI, Kleen Software combines artificial intelligence with technical product data and historical cost structures. This provides companies with a powerful platform for systematically analyzing material data, identifying cost drivers at an early stage, and making informed decisions.

The solution creates transparency where experience and manual analysis previously dominated, making economic optimization an integral part of the development process.

For more information, visit priceguard-ai.com.