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Success Stories
Ascentt Smart Contracts is an AI-powered solution transforming contract management in the automotive manufacturing industry by automating the extraction of key data elements from complex, unstructured documents across multiple formats and languages. Integrated with Azure OpenAI, Power BI, and Copilot, it provides secure, prompt-based analytics and dynamic dashboard generation without storing contractual data centrally, meeting strict legal and cybersecurity requirements. By minimizing manual labor and errors, it delivers substantial cost savings and scales seamlessly with your business growth, empowering you to focus on innovation and efficiency.
A multinational automotive giant faced significant challenges in navigating dynamic trade compliance regulations, predicting compliance risks, and ensuring compliant product launches. They partnered with Ascentt, whose AI-powered Trade Compliance Solution provided granular product projections, streamlined supplier surveys, accurate USMCA classification, advanced analytics, and an adaptable predictive sourcing model. The transformative results included timely government reporting, mitigation of $2 billion in annual tariff impact, fully vetted new product launches, and improved operational efficiency across all plants through monthly compliance monitoring. Ascentt’s solution enabled the company to overcome regulatory complexities, boost efficiency, and significantly reduce financial risks, revolutionizing their trade compliance strategy in the global marketplace.
In the rapidly evolving field of machine learning, organizations often struggle to scale and manage models effectively—only 10% of models make it into production, and 85% of ML projects fail to deliver expected results. This success story showcases how embracing MLOps automation transforms these challenges by unifying ModelOps, DataOps, and DevOps into a streamlined, automated workflow. By automating the end-to-end lifecycle—from data identification and feature engineering to model deployment and monitoring—companies experience an 80% improvement in data scientists’ efficiency. This not only accelerates time-to-market and enhances collaboration but also ensures consistent model quality and scalability. MLOps automation turns operational hurdles into opportunities for innovation, propelling organizations forward in the competitive AI-driven marketplace.
A leading automotive OEM faced significant challenges in assessing underbody rust due to manual, inconsistent, and resource-intensive processes. By leveraging advanced computer vision technology, they transformed rust detection into an automated, accurate, and scalable operation. Installing underbody scanners at key dealerships, the company increased vehicle surveys from 150 annually to approximately 10,000 in the first year. This innovation introduced a standardized rust ranking system, enhanced vehicle integrity, reduced costs, and improved customer satisfaction—setting a new industry standard for efficiency and safety.
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85% of all AI models and projects fail because of poor data quality. That’s what Gartner recently reported. In a day and age when Artificial Intelligence has everyone on their toes, statistics like these are truly heart-wrenching!
For many organizations, compliance is viewed in the same manner as an afterthought—simply a box to check when the auditors come knocking. This complacency is a time bomb waiting to go off. It is similar to cybersecurity breaches, where operations go down in the blink of an eye, or delayed procurement compliance that yields damaging outcomes. The consequences of ignoring regulations, be they on-premises processes or cloud-based workflows, aren’t just a gamble—it’s a direct threat to profitability as well as reputation.
Artificial Intelligence (AI) has far moved beyond being a speculative idea and now drives innovation across numerous industrial sectors. The success of AI projects relies on factors beyond data quality and algorithms, including deployment locations and technology utilization. A business entity encounters a critical choice regarding its AI workloads: Is it better to operate them in the cloud environment or on-premise infrastructure? Every decision presents inherent trade-offs involving scalability, security costs, and flexibility. We shall deconstruct these components while examining industry-specific factors to assist you in determining the optimal path for your organization.