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In your industry, specifications, requirements and framework agreements of OEM customers have to be checked regularly with high manual effort and time pressure. The content of many requirements and clauses differs only slightly from those of projects that have already been completed – yet they have to be read and evaluated again and again.
And this is exactly where our AI semantha® can help: It understands text based on its meaning, regardless of the choice of words – so you can find the content you are looking for, no matter how it is worded – out of the box and without extensive training with your data.
Specifications & requirements: semantha® supports you in processing new specifications and uses your historical, already evaluated requirements for this purpose. In just a few seconds semantha® reads through the new documents and compares them with your knowledge – regardless of the wording.
Framework contracts of OEM customers: semantha® supports you in checking framework contracts. In just a few seconds, the content of contracts can be compared with each other and examined for specific clauses – regardless of the wording.
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Time saving
With semantha®, our reference customers can save an average of 40% time in answering customer requirements and in checking framework agreements. -
Cost saving
Time is money – Every minute you save in your company you can spend on more value-added tasks. Not only do you save money on faster processing, you can also process and accept more requests. -
Risk minimization
Do you sometimes not have the possibility to check all customer documents in detail due to high time pressure? With semantha’s assistance you can increase your attention to detail and minimize potential risks.
The most frequently implemented use cases in automotive suppliers.
Requirements & Specification Analysis
Requirements specifications and requirements have to be checked regularly with high manual effort and time pressure. Many requirements and clauses differ only slightly in content from those from projects that have already been completed – nevertheless they have to be read and evaluated again and again.
Correspondence Automation
In your company masses of correspondences have to be processed and allocated every day. Each mail must be analysed at great expense before the next step can be taken. It is quite possible to assign certain categories, but the problem is that similar facts are formulated very differently, which apparently makes it impossible to automate this process.
OEM and Supplier Contract Analysis & Comparison
In your company, Framework contracts of OEM customers have to be reviewed regularly and with a high manual effort. In most cases, the content to be reviewed is similar. The problem is that every contract is worded and structured differently, which makes automation impossible.
These use cases might also interest you…
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Alignment of standards
The necessary comparison with internal and external standards (e.g. norms, applicable documents and industry standards) is taken into account by semantha® during the requirements analysis.
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Consolidation of documents
Harmonization of company-wide regulations, such as company agreements, work instructions & SOP’s across different locations
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CV Matching
With semantha® we have developed an AI tool that overcomes the weaknesses of conventional matching tools. The application does not compare whether certain terms occur in documents, but reads the documents at the level of meaning. And it does so within a few moments.
- Ranking of top candidates at the touch of a button
- Overview of the best internal candidates for any skills
- More objective selection of candidates
- Acceleration of the selection process by up to 50%
- More time for interviews to ensure human fit
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Tender Analysis
In your company, tenders have to be checked and evaluated regularly and with a high manual effort. In most cases, the content to be reviewed is similar. The problem is that every tender is worded and structured differently, which makes automation impossible.
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Knowledge Management
Your company has large masses of unstructured text documents. One of the daily challenges is to search these documents for specific content, whereby normal search functions only offer a simple “keyword search” that does not lead to the desired results. This is particularly due to the fact that, on the one hand, it is not known exactly how a topic was formulated and, on the other hand, the same topics are also formulated quite differently in the various documents.
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