When you Google something, have you ever wondered how it can provide specific results for you to explore? The answer is semantic technology, specifically the famous Knowledge Graph in the case of Google. The main goal of semantic technologies is to help machines understand data, and they are now enabling an evolution from simple relational database querying to the inference of logical relationships.
To put this shift into context, organizations can now deploy a solution that lets them search for, uncover, and immediately act on claimable technology service cost savings. During the short, medium, and long-term, large companies could save as much as 50% of their overall technology service spend, all thanks to this new way of interrogating billing data.
Why are businesses losing so much money?
Organizations are now spending more money on services than on goods. The result of this is a mass of convoluted invoices and contracts, many relating to telecoms, ICT, and other technology services. The complicated terminology, codes, and layouts associated with this kind of billing makes these documents practically impossible for teams of individuals to process manually, causing a sharp decline in data hygiene. With bad data being passed between various siloed teams, service value is allowed to continuously fall through the cracks.
Companies that wanted billing transparency in the past had to resort to calling in independent auditors, which typically resulted in an expensive, incomplete outcome. This comes down to the fact that traditional audits involve manual data analysis, for which auditors would often charge a hefty cut of any cost savings that were made.
Understanding the Semantic Web of Billing
Made possible by the technology that is bringing ‘Web 3.0’ to life, semantic technology can also be applied in a highly targeted way. Versions of it can be implemented with a focus on specific domains, where it can be used to map out all the concepts in closed domains like telecom, IT procurement, billing, and contracts. This makes it possible to draw parallels between these closed domains in an autonomous way, highlighting costly disparities between spend and value.
Thinking Machine Systems has developed a solution that could be considered the Semantic Web of cost savings. Using AI and machine learning technology, the solution can automatically load any volume of complex PDF invoices and immediately identify claimable cost saving opportunities. This can be carried out regardless of language or supplier, equipping you to personalize and refine your approach to data intelligence and long-term sourcing strategies.
Matching price with performance
To bring the value of this solution into sharp focus, our AI driven technology ensures that optimal pricing and performance needs are matched. The immediate savings this generates are beneficial to your bottom line, but the real advantages are unlocked by the data intelligence it enables. Armed with capabilities comparable to the Semantic Web, your procurement teams can finally achieve the right insights at the right time, which executives can use to easily optimize your sourcing strategy. Thinking Machine Systems harnesses the technologies of the Semantic Web to provide an automated professional audit. Not only is this a cheaper, faster return-on-investment, but the solution stays with you to ensure successful outcomes now and in the future. We also offer you the opportunity to explore the data and execute on savings opportunities internally, or we can handle the process in an end-to-end way for you.
