Caju AI empowers customer-facing workforces through an outstanding use of generative AI’s capabilities. It’s not a mystery that customer interactions are a priceless source of insights that can help companies drastically enhance various areas of their products, services, and operations. Caju AI allows organizations to unlock this potential on a level that was never achievable before. By integrating generative AI into digital communication channels, the tool helps organizations understand this ocean of unstructured data, organize it, and draw profound, actionable conclusions from it.
The client reached out to us at the beginning of 2023 with a clear goal: building a SaaS-based solution that would redefine the nature of customer engagement and operational excellence with the power of generative AI. Having their ideas already validated through a functional prototype, they needed a tech partner with a truly collaborative approach to help them bring their product’s first fully functioning version to life as quickly as possible.
One of our first tasks was to prepare comprehensive designs for the platform. It was crucial that we craft UI and UX with utmost care — to ensure that, despite its complexity, the product would look great and be easy to interact with.
Choosing the right tech stack and a proper approach to building such a unique SaaS solution was vital to its success. We needed to propose an architecture that would guarantee the proper functioning of every component of Caju AI.
Creating a SaaS platform with full tenancy isolation required an excellent, highly scalable, and secure infrastructure. We had to carefully design and then build it to ensure the platform could massively scale to serve hundreds of thousands of end users.
To serve its purpose, Caju AI had to be able to access tools used for customer interactions (meaning: data sources for generative AI). Our job was to make it possible. We were to start with WhatsApp & ensure that other tools could be added later on.
Unlike many, Caju AI’s innovativeness wasn’t just about “being powered by generative AI.” It was about how AI can be used and extended to unlock a new world of possibilities for customer-facing businesses. The client’s goal was to create a truly revolutionary platform that would redefine the nature of customer interactions, turn them into actionable intelligence, and translate into improving companies’ operational efficiency.
One of Caju AI’s fundamental goals was to allow companies to access business insights hidden within customer interactions. Due to the lack of appropriate tools able to process those interactions as a whole and structure such an ocean of unorganized data, something like that was never achievable before.
Supported by the power of data drawn from customer interactions, Caju AI was supposed to help companies lift their customer service quality to an unprecedented level. It was meant to open up a whole new world of hyper-personalization and change how we understand the concept of “individual approach.”
Being aware of the incredible pace of developments in the generative AI field, Caju AI’s founders knew that time to market could be a dealbreaker for their product’s success.
To ensure Caju AI stays ahead of the fast-growing competition and enters the market as a pioneer product, we had to prepare it for launch as soon as possible and ensure its high quality from Day 1.
Ensuring a short time to market doesn’t always have to be a challenge. At Neoteric, it’s rather a standard we strive to keep. Neither was it a novel concept to Caju’s founders — they’re all seasoned entrepreneurs, so “making things happen” is how they operate. However, building such a complex product with high requirements within barely a few months was challenging for all of us. But it was a quest we were all fully committed to succeed at!
Same as with the short time to market, incorporating the test users’ feedback into the development process doesn’t always have to be a challenge. However, it can definitely become one when there’s no time to spare — because, well, gathering that feedback typically requires some amount of waiting. Nevertheless, being well aware of its great value, we decided to stick to this approach.
Making generative AI fulfill specific tasks is one thing, but ensuring it’s doing it well (e.g., its outputs are correct and free from hallucinations) is an entirely different story. But in Caju AI, there was no space for mistakes. We had to ensure that Gen AI would do its job — and that it would do it correctly.
To ensure the accuracy and correctness of the insights generated by Caju AI, we:
Working on AI-powered solutions for years, we know how important smooth, transparent communication and flexible approach are. Due to this project’s characteristics — tight timeframe, the necessity to incorporate users’ feedback at various stages of the development, etc. — these aspects were especially critical.
Aiming at the shortest possible time to market, we had to ensure every project step was perfectly thought-through. There was no space for steps back or reworks; thus, continuous communication and working hand-in-hand were the core of our cooperation with Caju AI.
We helped the client define what integrations were necessary to ensure all components of Caju AI would work as intended. And, naturally, we implemented them.
Despite the project’s complexity, heavy workload, and all the challenges connected to the product’s nature, we’ve successfully fulfilled all the tasks and got the platform ready for launch in as little as 8 months.
In the client’s words: “The platform stands out for its ability to offer actionable intelligence to customers in real-time, marking a significant milestone in our progress.”
“Our efforts have culminated in a series of early customers and a robust pipeline with numerous opportunities, indicating a solid product-market fit.”
“The responses from our customers have been overwhelmingly positive; it’s not uncommon for us to hear a resounding “WOW!” from users. This technology has been a game-changer, akin to equipping our customers with a team of a thousand analysts, liberating them from the mundane to focus on higher-level tasks.”