(US and Canada) Over the last ten years, we’ve seen an explosion of data and a fantastic response from the technology community to store, access, and apply it in specific contexts very successfully. In this phase, the market has been primarily building and iterating point solutions or AI built to solve specific problems. AI solutions built for businesses could accept a particular type of data, process it, and deliver output as long as the path was predetermined. Narrow AI worked very well in the short term as it demonstrated the ability of AI to learn, grow and deliver results in controlled environments. Fast forward to here and now, OpenAI and generative AI companies are taking the world by storm by making AI more accessible to all through simple queriable interfaces and much stronger AI engines that can decode data way more powerfully than before.

While this was a fantastic start to our trajectory of AI adoption, our collective assumptions that narrow AI scales, are being challenged in this decade. And while GenAI is blowing these assumptions open, they’re controlled by the Big 5 and directed at the consumerization of AI, not compliance & regulation-bound large enterprises.

In the initial stages of an AI project, systems succeed in controlled environments when they’re trained on particular data sets and deployed in production. As systems see more and more data over time in production, models ended up needing overhaul or continuous maintenance. Enterprises deployed 100s of 1000s of people, systems, resources, and time to continuously iterate on these models, and manage and maintain these AI systems. Teams of ML engineers and data scientists spent endless cycles iterating models based on continuously flowing real-time data. This whole process has neither been efficient nor scalable. Enterprises spend 10s of millions in real-time every year on continuous model building, server, and infra cost, cost of employees and talent churn, and lastly, pay the price for missed opportunities on both revenue growth as well as cost savings. To top it all, the ever-growing, long list of priorities, keeps teams from addressing the ugly data problem – a problem of data being inaccurate, half-baked, fragmented across systems, and just plain wrong in many other cases.

Our experiments with Narrow AI and broken data come to an end as we usher in a new era of scalable AI built on data-centric approaches to building intelligence. At Mad Street Den, we believe that we’re in the era of generalizable systems that scale with changes in patterns of workflows and human behavior and change as the data changes and evolves with time. What we need is context-aware AI systems that don’t focus on the point but on end-to-end workflows and continuous learning systems. AI that builds models in real-time, on the go as it sees data evolve.

Contextual search that searches through multi-modal spaces, bringing valuable and rich data to power intelligence for enterprise, regardless of the application. Generative models, large language models, and conversational AI all become part of this enterprise vocabulary, requiring AI to make decisions on when and where these get applied based on human interaction. This is the promise of Mad Street Den.

Vue.ai’s vision for Enterprise AI

We strive towards context-aware, end-to-end systems that automate entire workflows not just specific points in a system bringing orders of magnitude savings and efficiency on one hand, revenue growth, and faster time to market on the other.

1. We help create, unify and port clean enriched data for companies from their inventory, content, employee, and customer information into one centralized data platform;
2. Automate processes ranging from document processing to employee-centric workflows,
3. Create immersive and personalized customer experiences across journeys with the rich data we help create, truly enabling enterprises to focus on sustainable, meaningful growth with their customer base.

Vue.ai enables every organization to:

• Fix ugly data and build high-quality, clean, rich, standardized business data.
• Use that high-quality data in their digital experiences and to automate and optimize end to end journeys,
• Understand every user, customer or employee at a 1:1 level and build dynamically personalized journeys or workflows for them, ensuring they find just the right content every time.

Large enterprise teams both business and ML/engineering across the globe build a wide range of applications using our industry specific preset models, our enterprise-opinionated data platform, whether it’s powering up with our vertical-specific preset models, our plug-and-play AI solutions, or the DSML tools to help them build their own models.

Let’s make this real with an example –

In the fiercely competitive world of loan processing, every company aims to expedite the process while mitigating risks. The core challenge lies in digitizing and validating the vast array of information provided by customers, including KYC documents, bank statements, payslips, asset records, and credit card data, among others.

A meticulous document tampering check is crucial to maintain the integrity of the data. The primary objective is to assess whether an applicant meets the criteria for the desired loan. By matching the applicant’s data with the loan catalog, the system can determine the suitable loan options, interest rates, and tenure. Should an applicant not qualify for their intended loan, the system will consider other viable loan types and explore ways to enrich their data to enhance eligibility. For instance, a joint account with a spouse might bolster the applicant’s eligibility. The system also excels in suggesting products that align with the applicant’s preferences, such as travel credit cards or fuel cards, thereby offering multiple eligible options.

With the entire workflow automated, eligibility decisions are made close to real-time, with any suspicious activity promptly flagged for manual inspection by the credit team, creating a learning loop for continuous improvement. Even after approval, the system continually learns from borrowers’ behavior, incorporating feedback to refine the loan approval process.

Furthermore, the bank can leverage account data to identify potential customers who might benefit from a loan or a credit card, enabling them to reach out with tailored and relevant offers. Overall, this seamless and intelligent loan processing system exemplifies the integration of cutting-edge technology to serve customers and drive business growth.

Similarly, AI can be deployed to a variety of use cases like claims processing for insurance, healthcare documentation for healthcare staffing, logistics documentation, personalized product recommendations for ecommerce and much more.

Vue.ai is currently implementing AI Transformation projects at over 150 enterprises across 6+ industries in 20+ countries.  

What makes us different?

1. Someone (finally) cares enough to make sure your operating costs of deploying AI does not eat up your entire enterprise budget,
2. Your one-stop shop for creating automation-centric or customer-centric experiences
3. The only AI company that actually cleans, enriches and unifies your data before enabling your AI applications,
4. Creating joy across your enterprise and across all your teams making them all AI-native,
5. RoI like never before. We’re proud of our Total Economic Impact across every use case we power whether its revenue growth, cost saving, or shorter time to market