CIO Bulletin

Stradigi AI – Redefining the future of human decision-making in business

Stradigi AI is a leading North American SaaS AI business platform provider that enables organizations to bring business-accelerating AI projects to fruition, quickly. Its AI business platform, Kepler, bridges the AI talent gap, democratizing the use of AI and machine learning for any type of user by way of intuitive Automated Data Science Workflows, designed to generate results for hundreds of use cases across many verticals. The Kepler platform allows users to extract key insights from their data to improve customer experience, grow revenue, and gain efficiencies across multiple industries including CPG/FMCG, e-commerce, online platforms, manufacturing, financial services, logistics, and more.

Kepler for Data Teams

Discover easy-to-use tools that make it easy to build and deploy AI models, without needing to be a data scientist or wait for other teams. With Kepler, it’s easy to answer complex questions using the power of machine learning and deep learning. As organizations become increasingly data-driven, teams share a growing interest to tap into AI to extract meaningful insights from within their data. Game changing AutoML makes long development cycles, with specialized resources, a thing of the past. By performing the technical aspects of model development, Kepler makes it easy to build, train, and work with ML models, and frees data teams to spend more time learning about their data and analyzing insights. With built-in data science best practices, Kepler places the emphasis on AI insights and predictions, not technical implementation. Use point-and-click walkthroughs to generate up to 35 AI pipelines, built according to business context (for example whether the problem is related to churn, demand prediction, classification, etc) and meta information found within the dataset. Reliability metrics show when data analysts know when a model is ready for production, and what to do when it’s not. Easy-to-understand tools explain which factors influenced the predicted outcome. Upload new datasets and retrain existing models to quickly validate model performance and relevance in a changing environment.

MLOPS: Deploy and manage AI apps at scale

The true impact of AI insights happens when models are sent into production. Deploying insights to decision dashboards or enterprise systems is the aha moment when data turns into value. Robust MLOPs streamlines the process to increase the speed of model deployment and usability. A common MLOps environment encourages a collaborative environment for data scientists and machine learning engineers. Streamline and accelerate the integration and deployment of ML models with proper monitoring, validation, and governance. Activate batch or real-time insights with the deployment manager that packages and sends everything needed for the AI model to transition from the design and development environment to the production environment. Monitor production models to ensure consistent AI pipeline performance. Monitor data drift to ensure training and live data remain similar and the model continues to deliver as expected. Introduce new data to test the performance of an existing model, and easily swap out existing models in a single click when a newer version produces better performance.

Natural Language Understanding (NLU)

A vast amount of human-generated data is unstructured natural language – social media content, customer reviews, and academic papers to name a few. Reading, categorizing, and extracting what’s actually essential information would be extremely time-consuming for a person – and people are prone to bias and human error. NLU can process large amounts of unstructured data – taking just milliseconds per document. Imagine how long it would take a human to read hundreds of thousands of Instagram comments about your brand and categorize them by topic or by the different emotions your brand generates. Humans and machines are collaborating more than ever, and the ability for a machine to understand the intent behind human language and extract critical pieces of information – like sentiment, dates, names, and places – is what allows human-like virtual assistants, chatbots, and phone triage systems to exist. With NLU, computers understand the structure and meaning of human language. The human interprets and communicates the intent of a sentence for the computer to read, decipher and understand in a valuable manner.

NLU uses algorithms that transform human language (unstructured data) into a format those machines can understand (structured data). An entity is a particularly important piece of data, and natural language understanding uses entity recognition to extract specific information like dates, times, locations, numbers, or any other words that are considered important. NLU then analyses text to determine the meaning behind what a person says. It uses intent recognition to identify a user’s sentiment (attitude or feeling) and what they mean. NLU is designed to understand intent even with mispronunciations, grammatical mistakes, and other human errors.

Kepler doesn’t have just one strategy – it goes far beyond using the same algorithm for all cases. Instead, it analyzes the key characteristics of your text in order to decide which strategy to follow – from the text pre-processing to the machine learning algorithm to be used. After the pre-processing pipeline, Kepler chooses between a series of machine learning algorithms to complete the classification task.

The Leader Upfront

Basil Bouraropoulos is a Co-Founder and the of Stradigi AI. He is an accomplished executive with nearly two decades experience as a serial entrepreneur, angel investor and CEO of Stradigi AI, one of Canada’s largest applied AI solutions providers. He received his first big entrepreneurial break at age 24 and later on, decided to pay it forward by supporting and investing in the next generation of Canadian startups. By practising respect, trust and loyalty, he has developed successful relationships with leading experts from all different industries. In 2018, he concluded a joint venture agreement with the award-winning marketing communications company Cossette while also becoming the first Quebec-based AI solutions provider to partner with the prestigious Vector Institute for Artificial Intelligence.