Our singular focus is to create a truly distinctive procurement platform that delights users and provides sustainable positive impact to the organizations we serve. We achieve this by providing transparency and insights to leaders, streamlining and automating processes to improve efficiency, and driving bottom-line impact by unlocking savings potential.
FactWise S2P is modular, and accounts for batch production, goods receival workflows and quality checks. Our current users find it very intuitive and efficient (create Rfxs <1min) and love the dynamic and historic insights into supplier performance it offers. Teams can start using FactWise with their data immediately, and if required, we can seamlessly integrate with ERP or other systems within the month. We are committed to generating win-win outcomes for buyers and suppliers globally.

FactWise serves global product manufacturing companies and private equities.

NanoNets is a Deep Learning web platform that makes it easier than ever before to use Deep Learning in practical applications. It combines the convenience of a web-based platform with Deep Learning models to create image recognition and object classification applications for your business. You can easily build and integrate deep learning models using NanoNets’ API. You can also work with our pre-trained models which have been trained on huge datasets and return accurate results. NanoNets has leveraged recent advances in Deep Learning to build rich representations of data which are transferable across tasks. It’s as simple as uploading your input, generating the output and getting a functioning and highly accurate Deep Learning model for your AI needs. NanoNets is revolutionary because it allows you to train models without large datasets. With just 100 images you can train a model on our platform to detect features and classify images with a high degree of accuracy. NanoNets benefits you in four important ways: ● It reduces the amount of data needed to build a Deep Learning Model ● NanoNets handles the infrastructure for hosting and training the model, and for the run time ● It reduces the cost of running deep learning models by sharing infrastructure across models ● It is possible for anyone to build a deep learning model