Custom-engineered parts are among the most complex items in procurement. Their technical specifications are hidden in PDF drawings, knowledge often resides only in the minds of individual employees, and ERP systems provide little actionable information for informed price negotiations. In this webinar, Tacto demonstrates how industrial AI bridges this gap: from the automated extraction of geometric features from technical drawings to data-driven target price calculation.

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Jan Scholich (Product Manager at Tacto) and Armand Gall (Implementation Manager at Tacto) explain in the webinar how Tacto Intelligence automatically analyzes technical drawings, makes parts comparable, and derives concrete target prices from them.

The data problem with custom-engineered parts

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Custom-engineered parts present procurement with a fundamental data problem. ERP systems only store item numbers, brief descriptions, and prices. The actual cost information—such as geometric complexity, material, surface treatment, or tolerances—is buried within the technical drawing itself. Five items labeled "corner elbow" can be purchased from different suppliers at completely different prices, without procurement being able to determine whether the price differences are justified. The cost expertise required to evaluate such differences often exists only in the minds of experienced buyers. It is not mapped in the system and cannot be scaled. This is exactly where Tacto comes in, by creating a structured data foundation for custom-engineered parts.

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From drawing to digital part profile

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Tacto Intelligence automatically extracts relevant properties from technical drawings and converts them into a structured part profile. In the first step, known as "Level 0," basic properties are captured: material, weight, volume, surface treatment, holes, surfaces, and fits. Each part is assigned a vector that numerically maps its technical features and makes it comparable to other parts. Furthermore, the drawing itself is stored as a visual embedding. This captures geometric details that go beyond the extracted individual features, such as tolerance classes and shape complexity. This creates a comprehensive digital profile for every custom-engineered part.

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Identifying similar parts and calculating target prices

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Based on the extracted part profiles, Tacto performs an analysis across the entire part database. Similar parts are identified, price differences are made visible, and they are placed in relation to technical differences. The potential for savings grows disproportionately with the number of parts analyzed, as more comparison options are created. For target price calculation, Tacto Intelligence also incorporates context data from the SRM: supplier performance, complaint rates, and current capacity information. External market data is also integrated, such as trends in raw material prices like the LME nickel price. From this, the system derives recommendations for action and proactively alerts procurement to potential renegotiation opportunities. Importantly, Tacto is not intended as a theoretical should-costing tool; instead, it derives target prices from real transaction data and market developments.

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Bridging the gap between engineering and procurement

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Another aspect of the webinar is the cross-departmental use of drawing data. The analysis not only reveals potential savings for procurement but can also create added value in engineering. Automated duplicate part detection helps engineers identify existing parts instead of designing new ones. This reduces variety and optimizes procurement costs from the very beginning.

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Conclusion

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This webinar demonstrates how industrial AI systematically unlocks previously hidden knowledge from technical drawings. By combining automated data extraction, cross-article comparisons, and external market data, it generates well-founded target prices that provide a new data-driven foundation for purchasing turned and milled parts.

Armand Gall and Jan Scholich from Tacto explain how industrial AI automatically analyzes technical drawings, extracts geometric features, and derives target prices for procurement. The webinar outlines the journey from data extraction and similar-part recognition to proactive recommendations, highlighting how engineering and purchasing can benefit from a shared data foundation.