PartSpace is an AI-powered procurement platform that analyzes technical drawings and 3D models. It extracts key details like materials, tolerances, and processes, links them with ERP and purchasing data, and delivers actionable insights for cost savings and supplier optimization.

Procurement managers, cost engineers, supply chain leaders, design engineers, and C-level executives benefit from faster decisions, reduced costs, and stronger supplier strategies.

PartSpace is used by manufacturing-driven industries such as aerospace, automotive, machinery, electronics, robotics, and medical technology.

Unlike generic e-procurement systems, PartSpace understands technical drawings and connects them directly to procurement and cost data, making decisions based on facts rather than assumptions.

Both. Mid-sized companies benefit from quick cost savings, while large enterprises use PartSpace for supplier benchmarking, cost engineering, and global sourcing strategies.

PartSpace, PartSpace’s AI, is trained on real drawing and procurement data, not synthetic data. It interprets technical details like an expert cost engineer.

It supports them. PartSpace provides data-driven insights so procurement professionals can negotiate better, save time, and make strategic decisions.

Key sectors include aerospace , automotive, machinery, electronics , robotics, and medical technology.

Most clients see a return on investment within 2 months due to immediate procurement savings.

Savings of more than 10% on drawing-based components are common, with some cases reaching up to 21%.

It benchmarks part data against historical purchasing, supplier databases, market references and technical features to generate fact-based target prices.

Yes. It reduces waiting hours, streamlines supplier selection, and automates analysis, saving both labor and operational costs.

Yes. Automated analysis and supplier matching speed up RFQs and cut waiting time in procurement cycles.

It detects duplicates, identifies opportunities for volume bundling, and suggests supplier consolidation strategies.

Yes. AI-powered clustering identifies variants and duplicates, helping reduce complexity and costs.

Yes. It breaks down costs by material, processing, and volume, giving clear should-cost transparency.

Yes. It provides supplier-specific and market-wide benchmarks to reveal cost drivers and negotiation opportunities.

It transforms procurement into a data-driven strategic advantage that reduces risk, optimizes supply chains, and ensures competitiveness.

PartSpace is the AI core of PartSpace, trained to understand technical drawings and supplier data like a cost engineer, but at scale.

It automatically extracts materials, dimensions, tolerances, and processes to calculate costs and suggest alternatives.

Technical drawings (2D/3D), purchasing history, supplier lists.

Yes. The AI structures and enriches messy data, improving overall quality.

Yes. It connects with existing systems to sync purchasing and product data.

No. Drawings can be uploaded directly and analyzed without special CAD plugins.

Yes. It was built for enterprise-scale datasets with thousands of components.

Yes. Each company gets a custom-trained AI model based on their own data.

The more data it analyzes, the better its predictions, clustering, and supplier matching become.

It identifies suppliers by comparing part similarities and production capabilities, improving accuracy with each dataset.

It compares supplier prices, lead times, and capabilities against peers to highlight savings, based on their technical features, not just meta data.

Yes. AI-driven matching suggests suitable alternatives, reducing risk.

Yes. It provides insights into price trends, capacity, and cost competitiveness.

By mapping similar suppliers globally and offering diversification strategies.

Yes. With data-backed target prices and benchmarks, procurement has stronger leverage.

Yes. It flags cost anomalies and dependency risks based on data.

Yes. It identifies overlaps and suggests bundling to reduce complexity.

Yes. It analyzes costs by material, volume, and processing.

By comparing both internal and external data to create transparent benchmarks.

Yes. Transparent costing builds trust and efficiency in buyer-supplier collaboration.

Typically a few weeks, without disrupting ongoing operations.

No. Minimal IT effort is needed — even a PoC can run without IT.

Yes. A PoC with ~500 parts can prove measurable savings within weeks.

Your team uploads part data, PartSpace trains PartSpace, and results are delivered via dashboards.

No. It’s designed to be intuitive for procurement professionals.

It’s a modern, intuitive UI optimized for procurement workflows.

By showing quick, measurable ROI and providing easy-to-use features.

Yes. Both teams can view insights and align on cost and supplier choices.

Yes. It is cloud-based, supporting multi-location collaboration.

Yes. Real-time dashboards visualize costs, suppliers, and savings.

PartSpace uses enterprise-grade encryption and security standards.

Yes, it fully complies with European data privacy laws.

In secure data centers located in Germany.

Yes. Each client has a separate, custom-trained model.

Yes, it is ISO 27001 and ISO 9001 certified.

Regularly, including by external audits and global enterprise clients.

It automates should-costing and cost driver analysis, freeing engineers from manual work.

Yes. By comparing in-house vs. external supplier costs.

Engineers can upload early designs and receive cost estimates instantly.

Yes. It detects redundant variants and helps reduce part complexity.

Yes. Duplicate detection is one of its strongest features.

By analyzing complex parts with strict tolerances, it helps cut supplier costs while meeting standards.

Yes. Faster supplier identification and pricing shorten development cycles.

Yes. Engineers can test design alternatives early for cost impact.

Yes. It evaluates materials and processes for greener alternatives.

Yes. It maps suppliers worldwide and benchmarks regional costs.

Flexible and transparent — tailored to company size, dataset, and needs.

Subscription-based, ensuring continuous updates and AI improvements.

Yes, larger datasets and more users affect the license cost.

Yes, many companies start with a PoC to validate ROI.

Often within weeks, thanks to fast onboarding.