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Our partner wanted to develop a SaaS platform for a business idea. After struggling with tradeshow contacts management with no proper way to make sense of all the data , they wanted to help other businesses work smarter and unlock the potential their database.
We adopted an agile approach to develop features in sprints. We began with developing a data strategy and design prototypes to test market feedback. In less than 6 months, we deployed a mobile and web platform equipped with OCR technology, time-saving features and data analytics for users.
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Our partner wanted to help automobile workshops save money by finding spare parts at a lower cost while enabling suppliers to do more sales.
We develop an automated Request-For-Quotations (RFQ) platform to help match buyers with suppliers at the right cost. Development was built in a low-code environment which helped reduce bugs for quicker deployment.
Our retail conglomerate partner wanted to reduce their overheads in their supply chains. This would help them reduce inefficiencies and enjoy greater profits.
Using design thinking and data correlation methods, we gained clearer insights into their customers’ purchasing behavior and the supply chain blockers. With these insights, we helped our client reduce their overheads by 24% and optimize their distribution frequency to 48% of their network.
We designed a platform in a short time period for their managers to manage the overheads.
Our aerospace partner wanted to predict when jet engine faults that are likely to occur in advance. This would enable them to optimize resources around the world and save money.
Through a mix of partner consultation, data analysis and modelling, we developed a Deep Learning model that achieved over 84% accuracy in predicting fault sequences likely to occur over a period of time.
We also integrated this into a central platform which engineers and managers can analyze each specific jet engine.