Case Study: Efficient Data Integration for Education
Creating a data hub for a State Government is a unique experience!
Case Study: Improved Enrolment, Retention, and Student Success
Engaging Every Stakeholder, Driving Real Results!
Our team delivered transformative learning outcomes for US’ largest Cyber Charter School by leveraging an AI-enabled personalized learning platform; developed with modular architecture; and powered by custom interactive learning content.
Case Study: AI Powered Data Curation
For the past 5 years, this process required 30 days—we reduced it to just 7!
By automating the entire workflow with GenAI and LLMs, we enhanced efficiency, scalability, and accuracy for a leading US financial services company.
Case Study: Reconciliation Process Automation
From Manual to Automated!
We streamlined processes, boosted sales performance, and saved valuable time by driving 100% trade-sales tagging automation for a global asset management company. The result? Improved efficiency and a stronger competitive edge. That is the power of Data Analytics and AI Operalization.
Why do clients need a Data Analytics & AI Operationalization company?
Clients don’t care about Data Analytics AI or operations. They exclusively care about only three outcomes (the 3Es):
- Efficiency, measured in terms of reduced cost or enhanced scale
- Experience, measured in terms of enhanced speed or higher customer satisfaction
- Effectiveness, measured in terms of enhanced revenue or better end outcomes
Today (and for the next decade), it is impossible to deliver these outcomes without Data Analytics & AI.
However, 70%+ of investments in data analytics & AI will fail or under-deliver. Why?
Because – what gets built never gets injected into an operational workflow or never gets adopted by end users or customers. In other words, the AI solution stays unoperationalized. (In another post, I will cover the primary drivers for lack of operationalization with real examples.)
Current categories of solutions in the market don’t address this problem. Typically, solution providers lack the combination of – end-to-end talent stack, technology and methodology to operationalize data analytics and AI.
Over the last three decades, new specialized categories were created to leverage the underlying new transformative technology of that decade. For example, IT became a new category in 1990s; BPO in 2000s; Data Analytics in 2010s. Similarly, a new category is needed to leverage the impact of AI.
We call it Data Analytics & AI Operationalization.
Straive – The story behind the name
When clients ask me what our name ”Straive” means, I tell them how Straive brings together two divergent capabilities- critical to deliver impact today.
- First – “AI” at the center of everything we do. So we can deliver transformative enhancements to efficiency, experience and revenue;
- Second – The humility and experience to realize that AI is not the magic bullet. Operationalizing AI requires hard iterative work in the trenches: cleaning data; developing insights; building advanced models; integrating them into legacy workflows and experts-in-loop since no AI works out of the box. So, without “striving”, there can be no AI impact!
Over the past 24 months, we have made significant progress against our goal of helping our clients operationalize Data Analytics and AI. We are embedding AI into exisiting client workflows; winning new clients by leveraging our domain-rich AI foundry accelerators; and our teams across the world have started experiencing how “transforming with AI” can also be a lot of fun!
In alignment with this, the Straive logo now emphasizes these differentiated AI capabilities.
Straive’s updated website and logo perfectly reflect our mission of placing AI at the heart of every solution. Take a moment to explore our new site here: www.straive.com.
Case Study: Payments and Fraud
Even the best algorithms are useless without clean data streams!
In this client example, our e-commerce teams built foundational data assets to improve the customer experience and fraud detection.
Case Study: Churn Prediction & Success Enhancement
Award-winning work by our EdTech team at LearningMate! We won the Platinum award for learning analytics at what is considered the Oscars of EdTech.
We operationalized data analytics and AI to improve student retention rates and their learning experience by engaging multiple stakeholders simultaneously – Teachers, Administrators, Parents, and Students.
Case Study: Collections and Bad Debt Reduction
Our integrated team of collections operations, along with Straive data analytics and AI engineers, collected €115 Millions in 12-months for a leading publisher! The size of total Straive team – less than 20 people. That’s over €5 Million per person.
