A major electronics manufacturer manages a portfolio of more than 10,000 components. Requests to find alternatives to competitors’ products accounted for more than 13% of inquiries, creating a potential route to new sales.
Technica AI developed a specification-based candidate matching approach that reduces repetitive product research while leaving final technical validation to specialists. In proof-of-concept work, the team also reduced the time needed to create a reusable knowledge entry from about 90 minutes to approximately 10–20 minutes.
Product substitution inquiries are commercial opportunities.
Customers and distributors may need an alternative when an existing part becomes unavailable, fails, or no longer meets requirements. They often start with a competitor’s part number rather than a detailed new specification.
A timely, technically sound recommendation can give the manufacturer an opportunity to introduce one of its own components. However, identifying a candidate is only the first step toward establishing suitability.
Finding equivalent parts required extensive manual research.
Relevant information was spread across catalogs, PDFs, data sheets, and internal product records. Comparing components demanded specialist knowledge of the specifications and operating conditions that actually mattered.
Product names or part-number similarity could not establish interchangeability. Engineers still needed to verify material differences, technical limits, and fit for the intended application.
Capturing the results as reusable knowledge was also time-consuming: each entry took around 96 minutes before process improvement.
Automating candidate discovery while keeping technical review.
Technica AI structured specifications for the client’s components and competing products into comparable fields, then developed a system to shortlist potential alternatives from a part number or required conditions.
The interface highlights matched and differing specifications, helping specialists evaluate the shortlist instead of searching documents from the beginning.
Approved comparison outcomes can be retained as reusable knowledge, reducing repeated investigation when similar inquiries arise.
Knowledge-entry time fell to approximately 10–20 minutes in PoC tests.
The proof of concept evaluated matching across four product categories and approximately 1,000 products. This was a defined test scope, not a validation of the manufacturer’s entire portfolio.
In a related knowledge-creation workflow, a task that previously took roughly 90 minutes was completed in approximately 10–20 minutes in tested cases. The original workflow recorded approximately 96 minutes per entry, rounded here to about 90 minutes.
The measured benefit was more efficient knowledge preparation in the tested workflow; sales impact was not measured.
Faster technical responses can support sales opportunities.
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Faster candidate identification
Teams can begin with a structured shortlist rather than repeated catalog searches.
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More focused engineering review
Specialists can spend more time checking critical differences and application fit.
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More consistent comparisons
Candidates are assessed against shared technical criteria and documented differences.
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Reusable market knowledge
Approved part comparisons can support future inquiries and product planning.
From manual catalog research to reusable cross-reference knowledge.
Combining structured specifications with AI-assisted shortlisting gives technical and sales teams a faster starting point for replacement inquiries, while experts retain responsibility for confirming product suitability.