Turning scattered engineering IP into a searchable reuse catalog

Close-up of industrial machinery with springs, bolts, and metal components

An equipment manufacturer (~$400M revenue) builds highly customized hardware, but many subcomponents — its "platform technologies" — are reusable. Reuse ran at 60–70% within a business unit and far lower across them, because nobody could systematically search what already existed; Sales and Engineering relied on individual memory. A first quote for a new RFQ took two weeks to three months, as engineers re-developed components the company already owned — and time-critical tenders were being lost to faster competitors.

2 weeks → 1 hour

of RFQ research

~40%

faster first quotes

cross-BU reuse

The technologies lived across five disconnected systems with no shared key. ProSights ingested and extracted from each source — including reusable technologies buried inside larger customer projects — resolved them into one auditable, canonical catalog with a defined schema, and wired in the company's own requirements extractor as the front door. Now a new RFQ returns a ranked briefing pack: the most relevant reusable technologies, which requirements each satisfies and misses, links to source documentation, prior adaptation effort, and an indicative price from ERP history.


The Challenge

Reusable platform technologies scattered across five systems (requirements, ERP, CAD, wikis, drives) with no shared key — many not flagged as reusable at all, buried inside larger customer projects. Every RFQ began with weeks of archaeology, and tenders were lost to faster rivals.


The Approach

Ingest & extract from each source, detecting reusable technologies hidden inside customer projects. Unify into a canonical catalog with a defined per-family schema, resolving entities across systems despite inconsistent naming. Match new RFQ requirements against the catalog and recommend reuse candidates.


The Build

An auditable catalog where every field traces to a source; two matching modes — structured attribute comparison for crisp hardware specs, semantic matching for software modules; and the company's existing requirements extractor as the intake. A briefing pack, not an auto-pricer — the engineer still prices; the system collapses the research.


The Outcome

Sales and Engineering get automated reuse recommendations the moment an inquiry lands, with indicative pricing — faster initial engineering, shorter time-to-market, lower cost-to-quote, and a better shot at time-critical tenders. The catalog is a durable, owned asset.

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